Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering.Existing studies use either early or late fusion, but face limitations.Early fusion pre-aligns a table row with its associated passages, forming "stars," which often include irrelevant contexts and miss query-dependent relationships.Late fusion retrieves individual nodes, dynamically aligning them, but it risks missing relevant contexts.Both approaches also struggle with advanced reasoning tasks, such as column-wise aggregation and multihop reasoning.To address these issues, we propose HELIOS, which combines the strengths of both approaches.First, the edge-based bipartite subgraph retrieval identifies finer-grained edges between table segments and passages, effectively avoiding the inclusion of irrelevant contexts.Then, the query-relevant node expansion identifies the most promising nodes, dynamically retrieving relevant edges to grow the bipartite subgraph, minimizing the risk of missing important contexts.Lastly, the star-based LLM refinement performs logical inference at the star graph level rather than the bipartite subgraph, supporting advanced reasoning tasks.Experimental results show that HELIOS outperforms state-of-the-art models with a significant improvement up to 42.6% and 39.9% in recall and nDCG, respectively, on the OTT-QA benchmark.

Late-interaction based multi-vector retrieval systems have greatly advanced the field of information retrieval by enabling fast and accurate search over millions of documents.However, these systems rely on a naive summation of token-level similarity scores, which often leads to inaccurate relevance estimation caused by the tokenization of semantic units (e.g., words and phrases) and the influence of low-content words (e.g., articles and prepositions).To address these challenges, we propose TRIAL: Token Relations and Importance Aware Late-interaction, which enhances late interaction by explicitly modeling token relations and token importance in relevance scoring.Extensive experiments on three widely used benchmarks show that TRIAL achieves state-of-the-art accuracy, with an nDCG@10 of 46.3 on MSMARCO (in-domain), and average nDCG@10 scores of 51.09 and 72.15 on BEIR and LoTTE Search (out-of-domain), respectively.With superior accuracy, TRIAL maintains competitive retrieval speed compared to existing late-interaction methods, making it a practical solution for large-scale text retrieval.

To reduce hallucinations in large language models (LLMs), researchers are increasingly investigating reasoning methods that integrate LLMs with external knowledge graphs (KGs).Existing approaches either map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph; the former is fragile because noisy query graphs derail retrieval, whereas the latter is inefficient due to entitylevel reasoning over large graphs.In order to tackle these problems, we propose SAFE (Schema-Driven Approximate Distance Join For Efficient Knowledge Graph Querying), a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval.SAFE introduces two key ideas: (1) an Approximate Distance Join (ADJ) algorithm that refines LLM-generated pseudo query graphs by flexibly aligning them with the KG's structure; and (2) exploiting a compact schema graph to perform ADJ efficiently, reducing overhead and improving retrieval accuracy.Extensive experiments on WebQSP, CWQ and GrailQA demonstrate that SAFE outperforms state-of-the-art methods in both accuracy and efficiency, providing a robust and scalable solution to overcome the inherent limitations of LLM-based knowledge retrieval.

Multimodal document retrieval aims to retrieve query-relevant components from documents composed of textual, tabular, and visual elements.An effective multimodal retriever needs to handle two main challenges: (1) mitigate the effect of irrelevant contents caused by fixed, single-granular retrieval units, and (2) support multihop reasoning by effectively capturing semantic relationships among components within and across documents.To address these challenges, we propose LILaC, a multimodal retrieval framework featuring two core innovations.First, we introduce a layered component graph, explicitly representing multimodal information at two layers-each representing coarse and fine granularity-facilitating efficient yet precise reasoning.Second, we develop a lateinteraction-based subgraph retrieval method, an edge-based approach that initially identifies coarse-grained nodes for efficient candidate generation, then performs fine-grained reasoning via late interaction.Extensive experiments demonstrate that LILaC achieves state-ofthe-art retrieval performance on all five benchmarks, notably without additional fine-tuning.We make the artifacts publicly available at github.com/joohyung00/lilac.

Real-time analysis of graphs containing temporal information, such as social media streams, Q&A networks, and cyber data sources, plays an important role in various applications. Among them, detecting patterns is one of the fundamental graph analysis problems. In this paper, we study time-constrained continuous subgraph matching, which detects a pattern with a strict partial order on the edge set in real-time whenever a temporal data graph changes over time. We propose a new algorithm based on two novel techniques. First, we introduce a filtering technique called time-constrained matchable edge that uses temporal information for filtering with polynomial space. Second, we develop time-constrained pruning techniques that reduce the search space by pruning some of the parallel edges in backtracking, utilizing temporal information. Extensive experiments on real and synthetic datasets show that our approach outperforms the state-of-the-art algorithm by up to two orders of magnitude in terms of query processing time.

Recent efforts in learned cardinality estimation (CE) have substantially improved estimation accuracy and query plans inside query optimizers. However, achieving decent efficiency, scalability, and the support of a wide range of queries at the same time, has remained questionable. Rather than falling back to traditional approaches to trade off one criterion with another, we present a new learned approach that achieves all these. Our method, called ASM, harmonizes autoregressive models for per-table statistics estimation, sampling for merging these statistics for join queries, and multi-dimensional statistics merging that extends the sampling for estimating thousands of sub-queries, without assuming independence between join keys. Extensive experiments show that ASM significantly improves query plans under a similar or smaller overhead than the previous learned methods and supports a wider range of queries.

The continuous subgraph matching (CSM) problem aims to continuously detect patterns on a dynamic graph, with real-world applications such as fraud detection. Numerous methods have been proposed to address CSM, yet they lack fair comparisons. Furthermore, an existing unified framework for CSM shows misleading experimental results due to its suboptimal implementations. In this paper, we propose a new framework that generates CSM code from the logical and physical plans of delta queries with stacked views. By expressing each CSM method as a delta query plan, our framework enables fair comparisons of CSM methods. Through our comprehensive experiments, we make cause-and-effect arguments for the divergent performance trends from the previous papers and further analyze the individual impacts of various techniques on overall performance. Specifically, our CSM code for an old method significantly outperforms the most recent CSM method, CaLiG, by up to 48.6 times.

A database replay system (DRS) captures workloads on a production system and then replays them in a test system to test various system changes, avoiding any risk before realizing them in production. The dependency graph generation in a DRS is crucial in preserving output determinism while maximizing concurrency. The state-of-the-art dependency graph generation algorithm deployed in a commercial DBMS uses a generate-and-prune strategy. It first generates a dependency graph by performing backward scans for each request in a workload. It then prunes all redundant edges using an expensive, transitive reduction algorithm. However, we notice that this generates a large dependency graph that contains many redundant edges and its worst-case time complexity is quadratic to the number of requests in a workload. In order to solve these challenging problems, we formally propose four classes of dependency graphs for DRSs. We then present a stateful single forward scan algorithm, SSFS, to generate any class of dependency graphs by performing a single scan over all requests while succinctly maintaining states. Here, states refer to information that is stored and maintained for efficient dependency graph generation. We also propose the parallel SSFS to utilize the computation power with multi-core CPUs while balancing the loads. We implemented our DRS in a leading commercial DBMS. Extensive experiments using the TPC-C, SD benchmarks, and a real-world customer workload show that our DRS significantly improves the dependency graph generation time by up to two orders of magnitude, compared to the state-of-the-art.

GPU-accelerated relational query execution engines have parallelized the execution of a pipeline, a sequence of operators. For the parallelization, the engines evenly partition the tuples in a table that will be scanned by the pipeline's first operator (a scan), and each thread executes the pipeline for the tuples in a partition. However, this approach leads to load imbalances since an operator returns a varying number of output tuples per input tuple, particularly under non-uniform data distributions such as skewed join key values. The load imbalances are classified into intra- and inter-warp load imbalances (intra-WLIs and inter-WLIs) since 1) threads are grouped into warps and 2) every thread in a warp evaluates the same operator for an input tuple concurrently following a single-instruction-multiple-thread manner. In contrast, threads in different warps can evaluate different operators concurrently. Although load balancing techniques have been proposed, however, they fail to solve the load imbalances on various workloads. In this paper, we propose a query execution engine, Themis, named after the deity of fairness, which symbolizes balanced workloads within our context. Themis minimizes intra-WLIs and inter-WLIs across various workloads. First, Themis minimizes intra-WLIs by redistributing tuples between the threads in a warp and making the threads evaluate an operator only when all of them hold inputs. Second, Themis mitigates the inter-WLIs by redistributing the tuples of warps with heavy workloads to idle warps. To check whether a warp's workload is heavy, we propose a method to approximate the sizes of warps' workloads. Based on these approximations, Themis adaptively adjusts the threshold for determining a warp's workload as heavy. In a recent benchmark JCC-H, which introduces skewed join key distributions to TPC-H, Themis significantly alleviates the inter-WLIs and intra-WLIs, outperforming the runner-up by up to 379x.

A Database Replay System (DRS) captures workloads from a production system and subsequently replays them in a testing environment to verify correctness and performance. Prior to the replay process, DRS initially generates a dependency graph from the workload to ensure output determinism and to maximize replay concurrency in the testing system. However, the state-of-the-art inefficiently generates unnecessarily larger dependency graphs, creating a major bottleneck in the end-to-end pipeline. DoppelGanger++ is a new DRS supporting fast dependency graph generation. This demonstration illustrates how it captures and replays workloads, with a focus on efficiently generating compact dependency graphs. Specifically, we showcase the end-to-end database replay workflow using the complete database replay workload, accompanied by a web tool developed for our demo which can animate the dependency graph generation process and visualize important internal data structures.

Subgraph counting is a fundamental problem in understanding and analyzing graph structured data, yet computationally challenging. This calls for an accurate and efficient algorithm for Subgraph Cardinality Estimation, which is to estimate the number of all isomorphic embeddings of a query graph in a data graph. We present FaST est , a novel algorithm that combines (1) a powerful filtering technique to significantly reduce the sample space, (2) an adaptive tree sampling algorithm for accurate and efficient estimation, and (3) a worst-case optimal stratified graph sampling algorithm for hard instances. Extensive experiments on real-world datasets show that FaST est outperforms state-of-the-art sampling-based methods by up to two orders of magnitude and GNN-based methods by up to three orders of magnitude in terms of accuracy.

The schemalessness, one of the major advantages of JSON representation format, comes with high penalties in querying and operations by denying various critical functions such as query optimizations, indexing, or data verification. There have been continuous efforts to develop an accurate JSON schema discovery algorithm from a bag of JSON documents. Unfortunately, existing schema discovery techniques, being top-down algorithms, face challenges from the lack of visibility into children nodes of JSON tree. With absence of the information about lower-level JSON elements, top-down algorithms need to employ assumptions and heuristics to decide the schema type of nodes. However, such static decisions are often violated in datasets which causes top-down algorithms to perform poorly. To overcome this, we propose an algorithm, called ReCG, that processes JSON documents in a bottom-up manner. It builds up schemas from leaf elements upward in the JSON document tree and, thus, can make more informed decisions of the schema node types. In addition, we adopt MDL (Minimum Description Length) principles systematically while building up the schemas to choose among candidate schemas the most concise yet accurate one with well-balanced generality. Evaluations show that our technique improves the recall and precision of found schemas by as high as 47%, resulting in 46% better F1 score while also performing 2.11× faster on average against the state-of-the-art.

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer; the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR frame-work is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes. However, the expressive power of the state-of-the-art modulation is limited due to its inability to localize and capture fine-grained details of data entities such as specific pixels and rays. To address this issue, we propose a novel framework for generalizable INR that combines a transformer encoder with a locality-aware INR decoder. The transformer encoder predicts a set of latent tokens from a data instance to encode local information into each latent token. The locality-aware INR decoder extracts a modulation vector by selectively aggregating the latent tokens via cross-attention for a coordinate input and then predicts the output by progressively decoding with coarse-to-fine modulation through multiple frequency bandwidths. The selective token aggregation and the multi-band feature modulation enable us to learn locality-aware representation in spatial and spectral aspects, respectively. Our framework significantly outperforms previous generalizable INRs and validates the usefulness of the locality-aware latents for downstream tasks such as image generation.

We propose a new method for estimating the number of answers OUT of a small join query Q in a large database D, and for uniform sampling over joins. Our method is the first to satisfy all the following statements.

We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of six extended papers from the DASFAA 2023 conference.The International Conference on Database Systems for Advanced Applications (DASFAA) is a well-established international conference series that provides a forum for technical presentations and discussions among database researchers, developers, and users from academia, business, and industry, which showcases state-of-the-art research and development activities in the general areas of database systems, Web information systems, and their advanced applications.The conference's long history has established the event as the premier research conference in the database area.

Spark big data processing platform is heavily used in today's IT services for various critical applications such as machine learning tasks for service recommendations or massive volumes of raw sales data analysis. Spark is designed to deliver high performance by enabling a high degree of parallelism while processing various heavy-weight queries that require homogeneous operations on large data. However, it has been observed that workloads made of small and short-running queries coming from various sources are becoming dominant in practice. Unfortunately, the current Spark architecture is unfit to process workloads made of a large number of small queries optimally due to excessive I/Os with small computations. We present a technique, called QaaD, that addresses this problem fundamentally by applying i) transparent conversion of workloads made of small queries into one with large queries and ii) dynamic partition size adjustment for runtime overhead minimization. For this, we introduce a new abstraction, microRDD, to support our design of query merging, the embedding of queries as part of data, and an opportunistic sharing of common input data among queries. Comprehensive evaluation using real-world data shows that QaaD is able to deliver 10.6x to 36.6x speed-up against standard Spark executions for small query workloads.

For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a$256\times 256$image as$8\times 8$resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework out-performs the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images.

Given a set of data graphs and a query graph, graph isomorphism query processing is the problem of finding all the data graphs that are isomorphic to the query graph. Graph isomorphism query processing is a core problem in graph analysis of various application domains. In existing approaches, index construction or query processing takes much time as the graph sizes increase. In this paper, we propose an efficient algorithm for graph isomorphism query processing. We introduce the color-label distribution which represents the canonical coloring of a vertex-labeled graph. Based on degree sequences and color-label distributions, we introduce a two-level index, which helps us efficiently solve graph isomorphism query processing. Experimental results on real datasets show that the proposed algorithm is orders of magnitude faster than the state-of-the-art algorithms in terms of index construction time, and it runs faster than existing algorithms in terms of query processing time as the graph sizes increase.

Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose an effective image generation framework of \emph{Draft-and-Revise} with \emph{Contextual RQ-transformer} to consider global contexts during the generation process. As a generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a sequence of discrete code stacks. After code stacks in the sequence are randomly masked, Contextual RQ-Transformer is trained to infill the masked code stacks based on the unmasked contexts of the image. Then, we propose the two-phase decoding, Draft-and-Revise, for Contextual RQ-Transformer to generates an image, while fully exploiting the global contexts of the image during the generation process. Specifically. in the \emph{draft} phase, our model first focuses on generating diverse images despite rather low quality. Then, in the \emph{revise} phase, the model iteratively improves the quality of images, while preserving the global contexts of generated images. In experiments, our method achieves state-of-the-art results on conditional image generation. We also validate that the Draft-and-Revise decoding can achieve high performance by effectively controlling the quality-diversity trade-off in image generation.

Learned cardinality estimation (CE) has recently gained significant attention for replacing long-studied traditional CE with machine learning, especially for deep learning. However, these estimators were developed independently and have not been fairly or comprehensively compared in common settings. Most studies use a subset of IMDB data which is too simple to measure their limits and determine whether they are ready for real, complex data. Furthermore, they are regarded as black boxes, without a deep understanding of why large errors occur.

Visual graph query interfaces (VQI) facilitate non-programmers to query graph data effortlessly. The construction of these interfaces for large networks is typically not data-driven. That is, they do not exploit the underlying networks to automatically generate the contents of various panels of a VQI. Such data-driven construction has several benefits such as facilitating efficient top-down and bottom-up query formulation and portability of an interface across different application domains and sources. In this demonstration, we present a novel plug-and-play visual subgraph query interface construction engine called PLAYPEN that can be plugged on any large network G with a plug specification b to automatically generate the VQI for G that satisfies b by populating various components of the interface.

For stability and reliability of real-world applications, the robustness of DNNs in unimodal tasks has been evaluated. However, few studies consider abnormal situations that a visual question answering (VQA) model might encounter at test time after deployment in the real-world. In this study, we evaluate the robustness of state-of-the-art VQA models to five different anomalies, including worst-case scenarios, the most frequent scenarios, and the current limitation of VQA models. Different from the results in unimodal tasks, the maximum confidence of answers in VQA models cannot detect anomalous inputs, and post-training of the outputs, such as outlier exposure, is ineffective for VQA models. Thus, we propose an attention-based method, which uses confidence of reasoning between input images and questions and shows much more promising results than the previous methods in unimodal tasks. In addition, we show that a maximum entropy regularization of attention networks can significantly improve the attention-based anomaly detection of the VQA models. Thanks to the simplicity, attention-based anomaly detection and the regularization are model-agnostic methods, which can be used for various cross-modal attentions in the state-of-the-art VQA models. The results imply that cross-modal attention in VQA is important to improve not only VQA accuracy, but also the robustness to various anomalies.

Graph isomorphism is a core problem in graph analysis of various application domains. Given two graphs, the graph isomorphism problem is to determine whether there exists an isomorphism between them. As real-world graphs are getting bigger and bigger, applications demand practically fast algorithms that can run on large-scale graphs. However, existing approaches such as graph canonization and subgraph isomorphism show limited performances on large-scale graphs either in time or space. In this paper, we propose a new approach to graph isomorphism, which is the framework of pairwise color refinement and efficient backtracking. The main features of our approach are: (1) pairwise color refinement and binary cell mapping (2) compressed CS (candidate space), and (3) partial failing set, which together lead to a much faster and scalable algorithm for graph isomorphism. Extensive experiments with real-world datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of running time.

Modern IT systems rely heavily on log analytics for critical operational tasks. Since the volume of logs produced from numerous distributed components is overwhelming, it requires us to employ automated processing. The first step of automated log processing is to convert streams of log lines into the sequence of log format IDs, called log templates. A log template serves as a base string with unfilled parts from which logs are generated during runtime by substitution of contextual information. The problem of log template discovery from the volume of collected logs poses a great challenge due to the semi-structured nature of the logs and the computational overheads. Our investigation reveals that existing techniques show various limitations. We approach the log template discovery problem as search-based learning by applying the ILP (Inductive Logic Programming) framework. The algorithm core consists of narrowing down the logs into smaller sets by analyzing value compositions on selected log column positions. Our evaluation shows that it produces accurate log templates from diverse application logs with small computational costs compared to existing methods. With the quality metric we defined, we obtained about 21%-51% improvements of log template quality.

Subgraph query processing (also known as subgraph search) and subgraph matching are fundamental graph problems in many application domains. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we propose a new subgraph search algorithm using equivalences of vertices in order to reduce search space: (1) static equivalence of vertices in a query graph that leads to an efficient matching order of the vertices, and (2) dynamic equivalence of candidate vertices in a data graph, which enables us to capture and remove redundancies in search space. These techniques for subgraph search also lead to an improved algorithm for subgraph matching. Experiments show that our approach outperforms state-of-the-art subgraph search and subgraph matching algorithms by up to several orders of magnitude with respect to query processing time.

Graph pattern cardinality estimation is the problem of estimating the number of embeddings of a query graph in a data graph. This fundamental problem arises, for example, during query planning in subgraph matching algorithms. There are two major approaches to solving the problem: sampling and synopsis. Synopsis (or summary)-based methods are fast and accurate if synopses capture information of graphs well. However, these methods suffer from large errors due to loss of information during summarization and inherent assumptions. Sampling-based methods are unbiased but suffer from large estimation variance due to large sample space. To address these limitations, we propose Alley, a hybrid method that combines both sampling and synopses. Alley employs 1) a novel sampling strategy, random walk with intersection, which effectively reduces the sample space, 2) branching to further reduce variance, and 3) a novel mining approach that extracts and indexes tangled patterns as synopses which are inherently difficult to estimate by sampling. By using them in the online estimation phase, we can effectively reduce the sample space while still ensuring unbiasedness. We establish that Alley has worst-case optimal runtime and approximation quality guarantees for any given error bound ε and required confidence μ. In addition to the theoretical aspect of Alley, our extensive experiments show that Alley outperforms the state-of-the-art methods by up to orders of magnitude higher accuracy with similar efficiency.

With the rise of streaming data for dynamic graphs, large-scale graph analytics meets a new requirement of Incremental Computation because the larger the graph, the higher the cost for updating the analytics results by re-execution. A dynamic graph consists of an initial graph G and graph mutation updates Δ G$ of edge insertions or deletions. Given a query Q, its results $Q(G)$, and updates for Δ G$ to G, incremental graph analytics computes updates Δ Q$ such that Q($G \cup Δ G)$ = $Q(G)$ $\cup$ Δ Q$ where $\cup$ is a union operator. In this paper, we consider the problem of large-scale incremental neighbor-centric graph analytics (\NGA ). We solve the limitations of previous systems: lack of usability due to the difficulties in programming incremental algorithms for \NGA and limited scalability and efficiency due to the overheads in maintaining intermediate results for graph traversals in \NGA. First, we propose a domain-specific language, ŁNGA, and develop its compiler for intuitive programming of \NGA, automatic query incrementalization, and query optimizations. Second, we define Graph Streaming Algebra as a theoretical foundation for scalable processing of incremental \NGA. We introduce a concept of Nested Graph Windows and model graph traversals as the generation of walk streams. Lastly, we present a system \SystemName, which efficiently processes incremental \NGA for large graphs. Comprehensive experiments show that it effectively avoids costly re-executions and efficiently updates the analytics results with reduced IO and computations.

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

Despite the crucial role of cardinality estimation in query optimization, there has been no systematic and in-depth study of the existing cardinality estimation techniques for subgraph matching queries. In this paper, for the first time, we present a comprehensive study of the existing cardinality estimation techniques for subgraph matching queries, scaling far beyond the original experiments. We first introduce a novel framework called g-care that enables us to realize all existing techniques on top of it and that provides insights on their performance. By using g-care, we then reimplement representative cardinality estimation techniques for graph databases as well as relational databases. We next evaluate these techniques w.r.t accuracy on rdf and non-rdf graphs from different domains with subgraph matching queries of various topologies so far considered. Surprisingly, our results reveal that all existing techniques have serious problems in accuracy for various scenarios and datasets. Intriguingly, a simple sampling method based on an online aggregation technique designed for relational data, consistently outperforms all existing techniques.

Supergraph search is one of fundamental graph query processing problems in many application domains. Given a query graph and a set of data graphs, supergraph search is to find all the data graphs contained in the query graph as subgraphs. In existing algorithms, index construction or filtering approaches are computationally expensive, and search methods can cause redundant computations. In this paper, we introduce four new concepts to address these limitations: (1) DAG integration, (2) dynamic programming between integrated DAG and graph, (3) active-first search, and (4) relevance-size order, which together lead to a much faster and scalable algorithm for supergraph search. Extensive experiments with real datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of indexing time and query processing time.

Translating natural language to SQL (NL2SQL) has received extensive attention lately, especially with the recent success of deep learning technologies. However, despite the large number of studies, we do not have a thorough understanding of how good existing techniques really are and how much is applicable to real-world situations. A key difficulty is that different studies are based on different datasets, which often have their own limitations and assumptions that are implicitly hidden in the context or datasets. Moreover, a couple of evaluation metrics are commonly employed but they are rather simplistic and do not properly depict the accuracy of results, as will be shown in our experiments. To provide a holistic view of NL2SQL technologies and access current advancements, we perform extensive experiments under our unified framework using eleven of recent techniques over 10+ benchmarks including a new benchmark (WTQ) and TPC-H. We provide a comprehensive survey of recent NL2SQL methods, introducing a taxonomy of them. We reveal major assumptions of the methods and classify translation errors through extensive experiments. We also provide a practical tool for validation by using existing, mature database technologies such as query rewrite and database testing. We then suggest future research directions so that the translation can be used in practice.

Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high-performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of asymmetric-partition replication , so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the optimistic synchronous commit protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.

Existing binary join based plans may be suboptimal for important, emerging applications. Typical query optimizers enumerate plans using binary joins only. In this paper, we introduce the multi-way join aware optimizer in SAP HANA. The naive way to extend the existing query optimizer to be aware of multi-way joins ( m -way joins for short) is to enumerate m -way joins on top of a traditional binary join enumeration framework. However, many different binary joins correspond to the same m -way join. Thus, unnecessary join enumerations would be required for such naive integration. To solve this problem, we introduce the new concept of an m -way join unit and explain how the construction of join units is plugged into the SAP HANA query optimizer. We also provide a series of optimizer enhancements by exploiting m -way join unit characteristics. Using TPC-H and our customer workloads, we showcase the superiority of our m -way join aware optimizer.

Subgraph matching (or subgraph isomorphism) is one of the fundamental problems in graph analysis. Extensive research has been done to develop practical solutions for subgraph matching. The state-of-the-art algorithms such as \textsfCFL-Match and \textsfTurbo\textsubscriptiso convert a query graph into a spanning tree for obtaining candidates for each query vertex and obtaining a good matching order with the spanning tree. However, by using the spanning tree instead of the original query graph, it could lead to lower pruning power and a sub-optimal matching order. Another limitation is that they perform redundant computation in search without utilizing the knowledge learned from past computation. In this paper, we introduce three novel concepts to address these inherent limitations: 1) dynamic programming between a directed acyclic graph (DAG) and a graph, 2) adaptive matching order with DAG ordering, and 3) pruning by failing sets, which together lead to a much faster algorithm \textsfDAF for subgraph matching. Extensive experiments with real datasets show that \textsfDAF outperforms the fastest existing solution by up to orders of magnitude in terms of recursive calls as well as in terms of the elapsed time.

We study the subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them impractical for very large graphs. Most of them follow a synchronous model where the performance is often bottlenecked by the machine with the worst performance. Motivated by this, in this paper, we propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system. RADS first identifies results that can be found using single-machine algorithms. This strategy not only improves the overall performance but also reduces network communication and memory cost. Moreover, RADS employs a novel region-grouped multi-round expand verify & filter framework which does not need to shuffle and exchange the intermediate results, nor does it need to replicate a large part of the data graph in each machine. This feature not only reduces network communication cost and memory usage, but also allows us to adopt simple strategies for memory control and load balancing, making it more robust. Several optimization strategies are also used in RADS to further improve the performance. Our experiments verified the superiority of RADS to state-of-the-art subgraph enumeration approaches.

A dynamic graph is defined by an initial graph and a graph update stream consisting of edge insertions and deletions. Identifying and monitoring critical patterns in the dynamic graph is important in various application domains such as fraud detection, cyber security, and emergency response. Given a dynamic data graph and a query graph, a continuous subgraph matching system reports positive matches for an edge insertion and reports negative matches for an edge deletion. Previous systems show significantly low throughput due to either repeated subgraph matching for each edge update or expensive overheads in maintaining enormous intermediate results. We present a fast continuous subgraph matching system called TurboFlux which provides high throughput over a fast graph update stream. TurboFlux employs a concise representation of intermediate results, and its execution model allows fast incremental maintenance. Our empirical evaluation shows that TurboFlux significantly outperforms existing competitors by up to six orders of magnitude.

Existing distributed graph analytics systems are categorized into two main groups: those that focus on efficiency with a risk of out-of-memory error and those that focus on scale-up with a fixed memory budget and a sacrifice in performance. While the former group keeps a partitioned graph resident in memory of each machine and uses an in-memory processing technique, the latter stores the partitioned graph in external memory of each machine and exploits a streaming processing technique. Gemini and Chaos are the state-of-the-art distributed graph systems in each group, respectively.

A large body of research on subgraph query processing on large networks assumes that a query is posed in the form of a connected graph. Unfortunately, end users in practice may not always have precise knowledge about the topological relationships between nodes in a query graph to formulate a connected query. In this demonstration, we present a novel graph querying paradigm called partial topology-based network search and a query processing system called panda to efficiently find top-k matches of a partial topology query ( ptq ) in a single machine. A ptq is a disconnected query graph containing multiple connected query components . ptq s allow an end user to formulate queries without demanding precise information about the complete topology of a query graph. We demonstrate various innovative features of panda and its promising performance.

The k-medoids algorithm is one of the best-known clustering algorithms. Despite this, however, it is not as widely used for big data analytics as the k-means algorithm, mainly because of its high computational complexity. Many studies have attempted to solve the efficiency problem of the k-medoids algorithm, but all such studies have improved efficiency at the expense of accuracy. In this paper, we propose a novel parallel k-medoids algorithm, which we call PAMAE, that achieves both high accuracy and high efficiency. We identify two factors---"global search" and "entire data"---that are essential to achieving high accuracy, but are also very time-consuming if considered simultaneously. Thus, our key idea is to apply them individually through two phases: parallel seeding and parallel refinement, neither of which is costly. The first phase performs global search over sampled data, and the second phase performs local search over entire data. Our theoretical analysis proves that this serial execution of the two phases leads to an accurate solution that would be achieved by global search over entire data. In order to validate the merit of our approach, we implement PAMAE on Spark as well as Hadoop and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). The results show that PAMAE significantly outperforms most of recent parallel algorithms and, at the same time, produces a clustering quality as comparable as the previous most-accurate algorithm. The source code and data are available at https://github.com/jaegil/k-Medoid.

Modern in-memory database systems are facing the need of efficiently supporting mixed workloads of OLTP and OLAP. A conventional approach to this requirement is to rely on ETL-style, application-driven data replication between two very different OLTP and OLAP systems, sacrificing real-time reporting on operational data. An alternative approach is to run OLTP and OLAP workloads in a single machine, which eventually limits the maximum scalability of OLAP query performance. In order to tackle this challenging problem, we propose a novel database replication architecture called Asynchronous Parallel Table Replication (ATR). ATR supports OLTP workloads in one primary machine, while it supports heavy OLAP workloads in replicas. Here, row-store formats can be used for OLTP transactions at the primary, while column-store formats are used for OLAP analytical queries at the replicas. ATR is designed to support elastic scalability of OLAP query performance while it minimizes the overhead for transaction processing at the primary and minimizes CPU consumption for replayed transactions at the replicas. ATR employs a novel optimistic lock-free parallel log replay scheme which exploits characteristics of multi-version concurrency control (MVCC) in order to enable real-time reporting by minimizing the propagation delay between the primary and replicas. Through extensive experiments with a concrete implementation available in a commercial database system, we demonstrate that ATR achieves sub-second visibility delay even for update-intensive workloads, providing scalable OLAP performance without notable overhead to the primary.

Computing connected components (CC) is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single machine, there have been proposed a number of distributed graph processing methods. The representative ones for CC are Hash-To-Min and PowerGraph. Hash-To-Min focuses on minimizing the number of MapReduce rounds, but is still slower than in-memory methods, PowerGraph is a fast and general in-memory graph method, but requires a lot of machines for handling billion-scale graphs. We propose an ultra-fast parallel method DSP-CC, using only a single PC that exploits secondary storage like a PCI-E SSD for handling billion-scale graphs. It can compute connected components I/O efficiently using only a limited size of memory. Our experimental results show that DSP-CC significantly outperforms the representative methods including Hash-To-Min and PowerGraph.

Subgraph enumeration is important for many applications such as subgraph frequencies, network motif discovery, graphlet kernel computation, and studying the evolution of social networks. Most earlier work on subgraph enumeration assumes that graphs are resident in memory, which results in serious scalability problems. Recently, efforts to enumerate all subgraphs in a large-scale graph have seemed to enjoy some success by partitioning the data graph and exploiting the distributed frameworks such as MapReduce and distributed graph engines. However, we notice that all existing distributed approaches have serious performance problems for subgraph enumeration due to the explosive number of partial results. In this paper, we design and implement a disk-based, single machine parallel subgraph enumeration solution called DualSim that can handle massive graphs without maintaining exponential numbers of partial results. Specifically, we propose a novel concept of the dual approach for subgraph enumeration. The dual approach swaps the roles of the data graph and the query graph. Specifically, instead of fixing the matching order in the query and then matching data vertices, it fixes the data vertices by fixing a set of disk pages and then finds all subgraph matchings in these pages. This enables us to significantly reduce the number of disk reads. We conduct extensive experiments with various real-world graphs to systematically demonstrate the superiority of DualSim over state-of-the-art distributed subgraph enumeration methods. DualSim outperforms the state-of-the-art methods by up to orders of magnitude, while they fail for many queries due to explosive intermediate results.

While multi-version concurrency control (MVCC) supports fast and robust performance in in-memory, relational databases, it has the potential problem of a growing number of versions over time due to obsolete versions. Although a few TB of main memory is available for enterprise machines, the memory resource should be used carefully for economic and practical reasons. Thus, in order to maintain the necessary number of versions in MVCC, versions which will no longer be used need to be deleted. This process is called garbage collection. MVCC uses the concept of visibility to define garbage. A set of versions for each record is first identified as candidate if their version timestamps are lower than the minimum value of snapshot timestamps of active snapshots in the system. All such candidates, except the one which has the maximum version timestamp, are safely reclaimed as garbage versions. In mixed OLTP and OLAP workloads, the typical garbage collector may not effectively reclaim record versions. In these workloads, OLTP applications generate a high volume of new versions, while long-lived queries or transactions in OLAP applications often block garbage collection, since we need to compare the version timestamp of each record version with the snapshot timestamp of the oldest, long-lived snapshot. Thus, these workloads typically cause the in-memory version space to grow. Additionally, the increasing version chains of records over time may also increase the traversal cost for them. In this paper, we present an efficient and effective garbage collector called HybridGC in SAP HANA. HybridGC integrates three novel concepts of garbage collection: timestamp-based group garbage collection, table garbage collection, and interval garbage collection. Through experiments using mixed OLTP and OLAP workloads, we show that HybridGC effectively and efficiently collects garbage versions with negligible overhead.

Matrix factorization is one of the fundamental techniques for analyzing latent relationship between two entities. Especially, it is used for recommendation for its high accuracy. Efficient parallel SGD matrix factorization algorithms have been developed for large matrices to speed up the convergence of factorization. However, most of them are designed for a shared-memory environment thus fail to factorize a large matrix that is too big to fit in memory, and their performances are also unreliable when the matrix is skewed.

RDF data are used to model knowledge in various areas such as life sciences, Semantic Web, bioinformatics, and social graphs. The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core function of processing RDF data is subgraph pattern matching. There have been two completely different directions for supporting efficient subgraph pattern matching. One direction is to develop specialized RDF query processing engines exploiting the properties of RDF data for the last decade, while the other direction is to develop efficient subgraph isomorphism algorithms for general, labeled graphs for over 30 years. Although both directions have a similar goal (i.e., finding subgraphs in data graphs for a given query graph), they have been independently researched without clear reason. We argue that a subgraph isomorphism algorithm can be easily modified to handle the graph homomorphism, which is the RDF pattern matching semantics, by just removing the injectivity constraint. In this paper, based on the state-of-the-art subgraph isomorphism algorithm, we propose an in-memory solution, Turbo HOM++ , which is tamed for the RDF processing, and we compare it with the representative RDF processing engines for several RDF benchmarks in a server machine where billions of triples can be loaded in memory. In order to speed up Turbo HOM++ , we also provide a simple yet effective transformation and a series of optimization techniques. Extensive experiments using several RDF benchmarks show that Turbo HOM++ consistently and significantly outperforms the representative RDF engines. Specifically, Turbo HOM++ outperforms its competitors by up to five orders of magnitude.

Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are$\mathsf{Hash\hbox{-}To\hbox{-}Min}$and$\mathsf{PowerGraph}$.$\mathsf{Hash\hbox{-}To\hbox{-}Min}$is the state-of-the artdisk-baseddistributed method which minimizes the number of MapReduce rounds.$\mathsf{PowerGraph}$is the-state-of-the-artin-memorydistributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose theDisk-based Sequential access-oriented Parallel processing(DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components,$\mathsf{DSP\hbox{-}CC}$, which largely improves the performance through sequential disk scan andpage-level cache-conscious parallel processing. Extensive experimental results show that$\mathsf{DSP\hbox{-}CC}$1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.

Graph triangulation, which finds all triangles in a graph, has been actively studied due to its wide range of applications in the network analysis and data mining. With the rapid growth of graph data size, disk-based triangulation methods are in demand but little researched. To handle a large-scale graph which does not fit in memory, we must iteratively load small parts of the graph. In the existing literature, achieving the ideal cost has been considered to be impossible for billion-scale graphs due to the memory size constraint. In this paper, we propose an overlapped and parallel disk-based triangulation framework for billion-scale graphs, OPT, which achieves the ideal cost by (1) full overlap of the CPU and I/O operations and (2) full parallelism of multi-core CPU and FlashSSD I/O. In OPT, triangles in memory are called the internal triangles while triangles constituting vertices in memory and vertices in external memory are called the external triangles. At the macro level, OPT overlaps the internal triangulation and the external triangulation, while it overlaps the CPU and I/O operations at the micro level. Thereby, the cost of OPT is close to the ideal cost. Moreover, OPT instantiates both vertex-iterator and edge-iterator models and benefits from multi-thread parallelism on both types of triangulation. Extensive experiments conducted on large-scale datasets showed that (1) OPT achieved the elapsed time close to that of the ideal method with less than 7% of overhead under the limited memory budget, (2) OPT achieved linear speed-up with an increasing number of CPU cores, (3) OPT outperforms the state-of-the-art parallel method by up to an order of magnitude with 6 CPU cores, and (4) for the first time in the literature, the triangulation results are reported for a billion-vertex scale real-world graph.

Graphs are used to model many real objects such as social networks and web graphs. Many real applications in various fields require efficient and effective management of large-scale graph structured data. Although distributed graph engines such as GBase and Pregel handle billion-scale graphs, the user needs to be skilled at managing and tuning a distributed system in a cluster, which is a nontrivial job for the ordinary user. Furthermore, these distributed systems need many machines in a cluster in order to provide reasonable performance. In order to address this problem, a disk-based parallel graph engine called Graph-Chi, has been recently proposed. Although Graph-Chi significantly outperforms all representative (disk-based) distributed graph engines, we observe that Graph-Chi still has serious performance problems for many important types of graph queries due to 1) limited parallelism and 2) separate steps for I/O processing and CPU processing. In this paper, we propose a general, disk-based graph engine called TurboGraph to process billion-scale graphs very efficiently by using modern hardware on a single PC. TurboGraph is the first truly parallel graph engine that exploits 1) full parallelism including multi-core parallelism and FlashSSD IO parallelism and 2) full overlap of CPU processing and I/O processing as much as possible. Specifically, we propose a novel parallel execution model, called pin-and-slide. TurboGraph also provides engine-level operators such as BFS which are implemented under the pin-and-slide model. Extensive experimental results with large real datasets show that TurboGraph consistently and significantly outperforms Graph-Chi by up to four orders of magnitude! Our implementation of TurboGraph is available at ``http://wshan.net/turbograph}" as executable files.

Given a query graph q and a data graph g, the subgraph isomorphism search finds all occurrences of q in g and is considered one of the most fundamental query types for many real applications. While this problem belongs to NP-hard, many algorithms have been proposed to solve it in a reasonable time for real datasets. However, a recent study has shown, through an extensive benchmark with various real datasets, that all existing algorithms have serious problems in their matching order selection. Furthermore, all algorithms blindly permutate all possible mappings for query vertices, often leading to useless computations. In this paper, we present an efficient and robust subgraph search solution, called TurboISO, which is turbo-charged with two novel concepts, candidate region exploration and the combine and permute strategy (in short, Comb/Perm). The candidate region exploration identifies on-the-fly candidate subgraphs (i.e, candidate regions), which contain embeddings, and computes a robust matching order for each candidate region explored. The Comb/Perm strategy exploits the novel concept of the neighborhood equivalence class (NEC). Each query vertex in the same NEC has identically matching data vertices. During subgraph isomorphism search, Comb/Perm generates only combinations for each NEC instead of permutating all possible enumerations. Thus, if a chosen combination is determined to not contribute to a complete solution, all possible permutations for that combination will be safely pruned. Extensive experiments with many real datasets show that TurboISO consistently and significantly outperforms all competitors by up to several orders of magnitude.

Finding subgraph isomorphisms is an important problem in many applications which deal with data modeled as graphs. While this problem is NP-hard, in recent years, many algorithms have been proposed to solve it in a reasonable time for real datasets using different join orders, pruning rules, and auxiliary neighborhood information. However, since they have not been empirically compared one another in most research work, it is not clear whether the later work outperforms the earlier work. Another problem is that reported comparisons were often done using the original authors' binaries which were written in different programming environments. In this paper, we address these serious problems by re-implementing five state-of-the-art subgraph isomorphism algorithms in a common code base and by comparing them using many real-world datasets and their query loads. Through our in-depth analysis of experimental results, we report surprising empirical findings.

Ranked subsequence matching finds top-k subsequences most similar to a given query sequence from data sequences. Recently, Han et al. [12] proposed a solution (referred to here as HLMJ) to this problem by using the concept of the minimum distance matching window pair (MDMWP) and a global priority queue. By using the concept of MDMWP, HLMJ can prune many unnecessary accesses to data subsequences using a lower bound distance. However, we notice that HLMJ may incur serious performance overhead for important types of queries. In this paper, we propose a novel systematic framework to solve this problem by viewing ranked subsequence matching as ranked union. Specifically, we propose a notion of the matching subsequence equivalence class (MSEQ) and a novel lower bound called the MSEQ-distance. To completely eliminate the performance problem of HLMJ, we also propose a cost-aware density-based scheduling technique, where we consider both the density and cost of the priority queue. Extensive experimental results with many real datasets show that the proposed algorithm outperforms HLMJ and the adapted PSM [22], a state-of-the-art index-based merge algorithm supporting non-monotonic distance functions, by up to two to three orders of magnitude, respectively.

Graphs provide a powerful way to model complex structures such as chemical compounds, proteins, images, and program dependence. The previous practice for experiments in graph indexing techniques is that the author of a newly proposed technique does not implement existing indexes on his own code base, but instead uses the original authors' binary executables and reports only the wall clock time. However, we observed that this practice may result in several problems [6]. In order to address these problems, we have implemented all representative graph indexing techniques on a common framework called iGraph [6]. In this demonstration we showcase iGraph and its visual tools using several real datasets and their workloads. For selected queries of the workloads, we show several unique features including visual performance analysis.

SVM (Support Vector Machine) is a well-established machine learning methodology popularly used for classification, regression, and ranking. Recently SVM has been actively researched for rank learning and applied to various applications including search engines or relevance feedback systems. A query in such systems is the ranking function F learned by SVM. Once learning a function F or formulating the query, processing the query to find top-k results requires evaluating the entire database by F.So far, there exists no exact indexing solution for SVM functions. Existing top-k query processing algorithms are not applicable to the machine-learned ranking functions, as they often make restrictive assumptions on the query, such as linearity or monotonicity of functions. Existing metric-based or reference-based indexing methods are also not applicable, because data points are invisible in the kernel space (SVM feature space) on which the index must be built. Existing kernel indexing methods return approximate results or fix kernel parameters. This paper proposes an exact indexing solution for SVM functions with varying kernel parameters.We first propose key geometric properties of the kernel space -- ranking instability and ordering stability -- which is crucial for building indices in the kernel space. Based on them, we develop an index structure iKernel and processing algorithms. We then present clustering techniques in the kernel space to enhance the pruning effectiveness of the index. According to our experiments, iKernel is highly effective overall producing 1~5% of evaluation ratio on large data sets. According to our best knowledge, iKernel is the first indexing solution that finds exact top-k results of SVM functions without a full scan of data set.

BACKGROUND: As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users' requests for extracting information from the RDF data as well as the lack of users' knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. RESULTS: In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. CONCLUSIONS: Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

BACKGROUND: Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. METHODS: We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. RESULTS: By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. CONCLUSIONS: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

Commercial tuple extraction systems have enjoyed some success to extract tuples by regarding HTML pages as tree structures and exploiting XPath queries to find attributes of tuples in the HTML pages. However, such systems would be vulnerable to small changes on the web pages. In this paper, we propose a robust tuple extraction system which utilizes spatial relationships among elements rather than the XPath queries of the elements. Our system regards elements in the rendered page as spatial objects in the 2-D space and executes spatial joins to extract target elements. Since humans also identify an element in a web page by its relative spatial location, our system extracting elements by their spatial relationships could possibly be as robust as manual extraction and is far more robust than existing tuple extraction systems.

BACKGROUND: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. RESULTS: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. CONCLUSIONS: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.

Conventional object-relational database management system (ORDBMS) vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level (typically, SQL-level) interface. We call this mechanism loose-coupling . The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high performance is required. We have earlier proposed the tight-coupling architecture (Whang et al. 2002 , 2005 ) to satisfy this requirement. In tight-coupling , new data types and operations are integrated into the core of the DBMS engine in the extensible type layer . Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for 19 years. In this paper, we introduce the tightly-coupled spatial database features of Odysseus/OpenGIS. By taking advantage of tight-coupling, Odysseus/OpenGIS provides excellent performance in processing spatial queries as well as flexible concurrency control and recovery on spatial data. We show the performance through extensive experiments. Finally, we present sample applications of a geographical information system (GIS) implemented using Odysseus/OpenGIS.

Graphs are of growing importance in modeling complex structures such as chemical compounds, proteins, images, and program dependence. Given a query graph Q , the subgraph isomorphism problem is to find a set of graphs containing Q from a graph database, which is NP-complete. Recently, there have been a lot of research efforts to solve the subgraph isomorphism problem for a large graph database by utilizing graph indexes. By using a graph index as a filter, we prune graphs that are not real answers at an inexpensive cost. Then, we need to use expensive subgraph isomorphism tests to verify filtered candidates only. This way, the number of disk I/Os and subgraph isomorphism tests can be significantly minimized. The current practice for experiments in graph indexing techniques is that the author of a newly proposed technique does not implement existing indexes on his own code base, but instead uses the original authors' binary executables and reports only the wall clock time. However, we observe this practice may result in several problems. In order to address these problems, we have made significant efforts in implementing all representative indexing methods on a common framework called iGraph. Unlike existing implementations which either use (full or partial) in-memory representations or rely on OS file system cache without guaranteeing real disk I/Os, we have implemented these indexes on top of a storage engine that guarantees real disk I/Os. Through extensive experiments using many synthetic and real datasets, we also provide new empirical findings in the performance of the full disk-based implementations of these methods.

Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generalization performance of ranking functions in information retrieval. Due to fundamental differences between classification and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, has been recently proposed, which exploits importance of each feature and similarity between every pair of features. However, GAS must compute the similarity scores of all pairs of features, thus it is not scalable for high-dimensional data and its performance degrades on nonlinear ranking functions. This paper proposes novel algorithms, RankWrapper and RankFilter, which is scalable for high-dimensional data and also performs reasonably well on nonlinear ranking functions. RankWrapper and RankFilter are designed based on the key idea of Relief algorithm. Relief is a feature selection algorithm for classification, which exploits the notions of hits (data points within the same class) and misses (data points from different classes) for classification. However, there is no such notion of hits or misses in ranking. The proposed algorithms instead utilize the ranking distances of nearest data points in order to identify the key features for ranking. Our extensive experiments show that RankWrapper and RankFilter generate higher accuracy overall than the GAS and traditional Relief algorithms adapted for ranking, and run substantially faster than the GAS on high dimensional data.

The state of the art commercial query optimizers employ cost-based optimization and exploit dynamic programming (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The number of alternative QEPs enumerated by the DP query optimizer can increase exponentially, as the number of joins in the query increases. Recently, by exploiting the coming wave of multi-core processor architectures, a state of the art parallel optimization algorithm [14], referred to as PDPsva, has been proposed to parallelize the "time-consuming" DP query optimization process itself. While PDPsva significantly extends the practical use of DP to queries having up to 20-25 tables, it has several limitations: 1) supporting only the size-driven DP enumerator, 2) statically allocating search space, and 3) not fully exploiting parallelism. In this paper, we propose the first generic solution for parallelizing any type of bottom-up optimizer, including the graph-traversal driven type, and for supporting dynamic search allocation and full parallelism. This is a challenging problem, since recently developed, state of art DP optimizers such as DPcpp [21] and DPhyp [22] are very difficult to parallelize due to tangled dependencies in the join pairs they generate. Unless the solution is very carefully devised, a lot of synchronization conflicts are bound to occur. By viewing a serial bottom-up optimizer as one which generates a totally ordered sequence of join pairs in a streaming fashion, we propose a novel concept of dependency-aware reordering, which minimizes waiting time caused by dependencies of join pairs. To maximize parallelism, we also introduce a series of novel performance optimization techniques: 1) pipelining of join pair generation and plan generation; 2) the synchronization-free global MEMO; and 3) threading across dependencies. Through extensive experiments with various query topologies, we show that our solution supports any type of bottom up optimization, achieving linear speedup for each type. Despite the fact that our solution is generic, due to sophisticated optimization techniques, our generic parallel optimizer outperforms PDPsva tailored to size-driven enumeration. Experimental results also show that our solution is much more robust than PDPsva with respect to search space allocation.

A predictive spatiotemporal join finds all pairs of moving objects satisfying a join condition on future time and space. In this paper, we present CoPST, the first and foremost algorithm for such a join using two spatiotemporal indexes. In a predictive spatiotemporal join, the bounding boxes of the outer index are used to perform window searches on the inner index, and these bounding boxes enclose objects with increasing laxity over time. CoPST constructs globally tightened bounding boxes “on the fly” to perform window searches during join processing, thus significantly minimizing overlap and improving the join performance. CoPST adapts gracefully to large-scale databases, by dynamically switching between main-memory buffering and disk-based buffering, through a novel probabilistic cost model. Our extensive experiments validate the cost model and show its accuracy for realistic data sets. We also showcase the superiority of CoPST over algorithms adapted from state-of-the-art spatial join algorithms, by a speedup of up to an order of magnitude.

We study the problem of extracting flattened tuple data from streaming, hierarchical XML data. Tuple-extraction queries are essentially XML pattern queries with multiple extraction nodes. Their typical applications include mapping-based XML transformation and integrated (set-based) processing of XML and relational data. Holistic twig joins are known for the optimal matching of XML pattern queries on parsed/indexed XML data. Naïve application of the holistic twig joins to streaming XML data incurs unnecessary disk I/Os. We adapt the holistic twig joins for tuple-extraction queries on streaming XML with two novel features: first, we use the block-and-trigger technique to consume streaming XML data in a best-effort fashion without compromising the optimality of holistic matching; second, to reduce peak buffer sizes and overall running times, we apply query-path pruning and existential-match pruning techniques to aggressively filter irrelevant incoming data. We compare our solution with the direct competitor TurboXPath and other alternative approaches that use full-fledged query engines such as XQuery or XSLT engines for tuple extraction. The experiments using real-world XML data and queries demonstrated that our approach 1) outperformed its competitors by up to orders of magnitude, and 2) exhibited almost linear scalability. Our solution has been demonstrated extensively to IBM customers and will be included in customer engagement applications in healthcare.

Many commercial RDBMSs employ cost-based query optimization exploiting dynamic programming (DP) to efficiently generate the optimal query execution plan. However, optimization time increases rapidly for queries joining more than 10 tables. Randomized or heuristic search algorithms reduce query optimization time for large join queries by considering fewer plans, sacrificing plan optimality. Though commercial systems executing query plans in parallel have existed for over a decade, the optimization of such plans still occurs serially. While modern microprocessors employ multiple cores to accelerate computations, parallelizing query optimization to exploit multi-core parallelism is not as straightforward as it may seem. The DP used in join enumeration belongs to the challenging nonserial polyadic DP class because of its non-uniform data dependencies. In this paper, we propose a comprehensive and practical solution for parallelizing query optimization in the multi-core processor architecture, including a parallel join enumeration algorithm and several alternative ways to allocate work to threads to balance their load. We also introduce a novel data structure called skip vector array to significantly reduce the generation of join partitions that are infeasible. This solution has been prototyped in PostgreSQL. Extensive experiments using various query graph topologies confirm that our algorithms allocate the work evenly, thereby achieving almost linear speed-up. Our parallel join enumeration algorithm enhanced with our skip vector array outperforms the conventional generate-and-filter DP algorithm by up to two orders of magnitude for star queries-linear speedup due to parallelism and an order of magnitude performance improvement due to the skip vector array.

Commercial enterprise data warehouses are typically implemented on parallel databases due to the inherent scalability and performance limitation of a serial architecture. Queries used in such large data warehouses can contain complex predicates as well as multiple joins, and the resulting query execution plans generated by the optimizer may be sub-optimal due to mis-estimates of row cardinalities. Progressive optimization (POP) is an approach to detect cardinality estimation errors by monitoring actual cardinalities at run-time and to recover by triggering re-optimization with the actual cardinalities measured. However, the original serial POP solution is based on a serial processing architecture, and the core ideas cannot be readily applied to a parallel shared-nothing environment. Extending the serial POP to a parallel environment is a challenging problem since we need to determine when and how we can trigger re-optimization based on cardinalities collected from multiple independent nodes. In this paper, we present a comprehensive and practical solution to this problem, including several novel voting schemes whether to trigger re-optimization, a mechanism to reuse local intermediate results across nodes as a partitioned materialized view, several flavors of parallel checkpoint operators, and parallel checkpoint processing methods using efficient communication protocols. This solution has been prototyped in a leading commercial parallel DBMS. We have performed extensive experiments using the TPC-H benchmark and a real-world database. Experimental results show that our solution has negligible runtime overhead and accelerates the performance of complex OLAP queries by up to a factor of 22.

Clio is an existing schema-mapping tool that provides user-friendly means to manage and facilitate the complex task of transformation and integration of heterogeneous data such as XML over the Web or in XML databases. By means of mappings from source to target schemas, Clio can help users conveniently establish the precise semantics of data transformation and integration. In this paper we study the problem of how to efficiently implement such data transformation (i.e., generating target data from the source data based on schema mappings). We present a three-phase framework for high-performance XML-to-XML transformation based on schema mappings, and discuss methodologies and algorithms for implementing these phases. In particular, we elaborate on novel techniques such as streamed extraction of mapped source values and scalable disk-based merging of overlapping data (including duplicate elimination). We compare our transformation framework with alternative methods such as using XQuery or SQL/XML provided by current commercial databases. The results demonstrate that the three-phase framework (although as simple as it is) is highly scalable and outperforms the alternative methods by orders of magnitude.

Spatial joins find all pairs of objects that satisfy a given spatial relationship. In spatial joins using indexes, original-space indexes such as the R-tree are widely used. An original-space index is the one that indexes objects as represented in the original space. Since original-space indexes deal with extents of objects, it is relatively complex to optimize join algorithms using these indexes. On the other hand, transform-space indexes, which transform objects in the original space into points in the transform space and index them, deal only with points but no extents. Thus, optimization of join algorithms using these indexes can be relatively simple. However, the disadvantage of these join algorithms is that they cannot be applied to original-space indexes such as the R-tree. In this paper, we present a novel mechanism for achieving the best of these two types of algorithms. Specifically, we propose the new notion of the transform-space view and present the transform-space view join algorithm. The transform-space view is a virtual transform-space index based on an original-space index. It allows us to "interpret" or "view" an existing original-space index as a transform-space index with no space and negligible time overhead and without actually modifying the structure of the original-space index or changing object representation. The transform-space view join algorithm joins two original-space indexes in the transform space through the notion of the transform-space view. Through analysis and experiments, we verify the excellence of the transform-space view join algorithm. The transform-space view join algorithm always outperforms existing ones for all the data sets tested in terms of all three measures used: the one-pass buffer size (the minimum buffer size required for guaranteeing one disk access per page), the number of disk accesses for a given buffer size, and the wall clock time. Thus, it constitutes a lower-bound algorithm. We believe that the proposed transform-space view can be applied to developing various new spatial query processing algorithms in the transform space.

We propose the notion of tight-coupling [K. Whang et al., (1999)] to add new data types into the DBMS engine. In this paper, we introduce the Odysseus ORDBMS and present its tightly-coupled IR features (US patented). We demonstrate a Web search engine capable of managing 20 million Web pages in a non-parallel configuration using Odysseus.

Prefetching is an effective method for minimizing the number of fetches between the client and the server in a database management system. In this paper, we formally define the notion of prefetching. We also formally propose new notions of the type-level access locality and type-level access pattern. The type-level access locality is a phenomenon that repetitive patterns exist in the attributes referenced. The type-level access pattern is a pattern of attributes that are referenced in accessing the objects. We then develop an efficient capturing and prefetching policy based on this formal framework. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object-ids or page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i.e., if there is type-level access locality. Many navigational applications in object-relational database management systems (ORDBMSs) have type-level access locality. Therefore, our technique can be employed in ORDBMSs to effectively reduce the number of fetches, thereby significantly enhancing the performance. We also address issues in implementing the proposed algorithm. We have conducted extensive experiments in a prototype ORDBMS to show effectiveness of our algorithm. Experimental results using the 007 benchmark, a real GIS application, and an XML application show that our technique reduces the number of fetches by orders of magnitude and improves the elapsed time by several factors over on-demand fetching and context-based prefetching, which is a state-of-the-art prefetching method. These results indicate that our approach provides a new paradigm in prefetching that improves performance of navigational applications significantly and is a practical method that can be implemented in commercial ORDBMSs.

In video-on-demand (VOD) systems, as the size of the buer allocated to user requests increases, initial latency and mem-ory requirements increase. Hence, the buer size must be minimized. The existing static buer allocation scheme, however, determines the buer size based on the assumption that the system is in the fully loaded state. Thus, when the system is in a partially loaded state, the scheme allocates a buer larger than necessary to a user request. This paper proposes a dynamic buer allocation scheme that allocates to user requests buers of the minimum size in a partially loaded state as well as in the fully loaded state. The inherent diÆculty in determining the buer size in the dynamic buer allocation scheme is that the size of the buer currently be-ing allocated is dependent on the number of and the sizes of the buers to be allocated in the next service period. We solve this problem by the predict-and-enforce strategy, where we predict the number and the sizes of future buers based on inertia assumptions and enforce these assumptions at runtime. Any violation of these assumptions is resolved by deferring service to the violating new user request until the assumptions are satised. Since the size of the current buer is dependent on the sizes of the future buers, the size is represented by a recurrence equation. We provide a solution to this equation, which can be computed at the system initialization time for runtime eÆciency. We have performed extensive analysis and simulation. The results show that the dynamic buer allocation scheme reduces ini-tial latency (averaged over the number of user requests in service from one to the maximum capacity) to

We generalize the method of constructing windows in subsequence matching. By this generalization, we can explain earlier subsequence matching methods as special cases of a common framework. Based on the generalization, we propose a new subsequence matching method, General Match. The earlier work by Faloutsos et al. (called FRM for convenience) causes a lot of false alarms due to lack of point-filtering effect. Dual Match, recently proposed as a dual approach of FRM, improves performance significantly over FRM by exploiting point filtering effect. However, it has the problem of having a smaller allowable window size---half that of FRM---given the minimum query length. A smaller window increases false alarms due to window size effect. General Match offers advantages of both methods: it can reduce window size effect by using large windows like FRM and, at the same time, can exploit point-filtering effect like Dual Match. General Match divides data sequences into generalized sliding windows (J-sliding windows) and the query sequence into generalized disjoint windows (J-disjoint windows). We formally prove that General Match is correct, i.e., it incurs no false dismissal. We then propose a method of estimating the optimal value of the sliding factor J that minimizes the number of page accesses. Experimental results for real stock data show that, for low selectivities (10-6∼10-4), General Match improves average performance by 117% over Dual Match and by 998% over FRM; for high selectivities (10-3∼10-1), by 45% over Dual Match and by 64% over FRM. The proposed generalization provides an excellent theoretical basis for understanding the underlying mechanisms of subsequence matching.

Prefetching is an effective method for minimizing the number of round-trips between the client and the server in database management systems. We propose new notions of the type-level access locality and the type-level access pattern. We also formally define the notions of capturing and prefetching to help understand the underlying mechanisms. We then develop an efficient prefetching policy based on these notions and the framework. The type-level access locality is a phenomenon that repetitive patterns exist in the attributes referenced. The type-level access pattern is a pattern of attributes that are referenced in accessing the objects. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object-ids or page-ids of the objects accessed. However the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast even though the same objects are not accessed repeatedly our technique effectively prefetches objects if the same attributes are referenced repeatedly, i.e., if there is type-level access locality. Many navigational applications in object-relational database management systems (ORDBMSs) have type-level access locality. Therefore, our technique can be employed in ORDBMSs to effectively reduce the number of round trips, thereby significantly enhancing the performance.

A searc h condition in object-oriented queries consists of nested predicates, eac h of which is a predicate on a path expression.In this paper, w e present a new selectivit y estimation technique for nested predicates.Selectivity of a nested predicate, neste d selectivity, is de ned as the ratio of the number of quali ed objects of the starting class in the path expression to the total number of objects of the class.The new technique takes into account the eects of direct representation of manyto-many relationships.Many-to-many relationships frequently occur in object-oriented databases, but have n o t been properly handled in con ven tional selectivit yestimation techniques.F or many-to-many relationships, we generalize the block-hit function originally proposed by B. Yao allowing the cases where one object belongs to more than one block.The most signi cant advantage of our technique is that the accuracy of the estimation is far enhanced with only a small additional overhead.We present an ecient method for obtaining the statistical information that is needed for our estimation technique.We analyze the accuracy of our estimation technique and compare the result with those of conven tional ones.The experimental result shows there is a signi cant d eviation in the estimation obtained by c o n ven tional ones, con rming the advan tage of our technique.

We present a tunable two-dimensional class hierarchy indexing technique (2D-CHI) for object-oriented databases. We use a two-dimensional file organization as the index structure. 2D-CHI deals with the problem of clustering objects in a two-dimensional domain space consisting of the key attribute domain and the class identifier domain. In conventional class indexing techniques using one-dimensional index structures such as the B/sup +/-tree, the clustering property is exclusively owned by one attribute. These indexing techniques do not efficiently handle the queries that address both the attribute keys and the class identifiers. 2D-CHI enhances query performance by adjusting the degree of clustering between the key value domain and the class identifier domain based on the precollected usage pattern. For performance evaluation, we first compare 2D-CHI with the conventional class indexing techniques using an analytic cost model based on the assumption of uniform object distribution, and then verify the cost model through experiments using the multilevel grid file as the two-dimensional index. We further perform experiments with nonuniform object distributions. Our experiments show that our proposed method does indeed build optimal class index structures regardless of query types and object distributions. We strongly believe that our paper significantly contributes to building a self-tunable database system by supporting automatically tunable index structure.