Graph embedding, which maps graph nodes to lowdimensional vectors, is a widely used technique for graph representation learning. However, most existing graph embedding models suffer from high memory consumption, limiting their scalability to large graphs. Heterogeneous memory systems that combine DRAM and Persistent Memory (PM) offer new opportunities for scaling up memory capacity. Despite this advantage, the performance gap (on the order of 5x) between DRAM and PM is magnified (by 3.3-4.2x) under non-uniform memory access (NUMA) architecture. Additionally, the inherent sparsity of graphs induces numerous random accesses in the fundamental Sparse Matrix and Dense Matrix Multiplication (SpMM) operations of graph embedding, hindering high-performance heterogeneous memory processing. To address these challenges, this paper presents OMeGa that focuses on Optimizing heterogeneous Memory processing for large-scale Graph embedding. OMeGa leverages an entropy-aware thread allocation, simultaneously achieving workload balancing and tail latency reduction across threads. It also incorporates a workload feature-aware prefetcher to alleviate random accesses during streaming heterogeneous processing. In addition, OMeGa devises a NUMA-aware data placement, aiming to minimize the adverse impact of NUMA on heterogeneous memory. The experiments conducted on billion-scale graphs demonstrate that OMeGa exhibits an average acceleration of 32.03x with strong scalability. This pioneering capability enables the efficient generation of large-scale graph embeddings, free from the memory size constraints and performance disparities typically encountered in heterogeneous memory systems.

The learned index technique has been widely explored as a strong competitor to traditional indexes. It adopts static learning-based models to fit the distribution of sorted data and locate keys through predictions, which shows outstanding query speed. However, frequent retraining is required when it comes to concurrent insertion scenarios. Despite existing studies introducing sparse slots and delta buffers to mitigate this effect, the read-write performance of the learned index still falls short of expectations, especially in concurrent conditions. In this paper, we first propose a novel hybrid index scheme that combines a read-efficient learned index with an insert-efficient Adaptive Radix Tree (ART) to realize high performance for read-write scenarios. However, it is not trivial due to expensive model prediction errors, complicated model hierarchy, and redundant node traversals. Therefore, we then introduce ALT-index, an efficient hybrid learned index with high concurrency for memory database systems. ALT-index highlights a delicate two-tier architecture where linear data are stored in the learned index without prediction errors and conflict data are hosted in the lower layer as an optimized ART. Besides, we develop a Greedy Pessimistic Linear (GPL) algorithm to support flattened data structures for concurrency. In the optimized ART layer, we introduce a fast and compact pointer buffer to further improve the overall performance. Experimental results conducted on various real-world datasets with 32 threads illustrate that ALT-index improves performance by up to 1.9x, 2.1x, and 2.3x compared with ALEX+, FINEdex, and XIndex in read-write-balanced scenarios, respectively.

Graph embedding maps graph nodes to low-dimensional vectors and is widely used in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based, and pipeline-optimized graph embedding framework,$\sf{DistGER-Pipe}$DistGER−Pipe, which scales to embed billion-edge graphs.$\sf{DistGER-Pipe}$DistGER−Pipeincrementally computes information-centric random walks to reduce redundant computations for more effective and efficient graph embedding. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines.$\sf{DistGER-Pipe}$DistGER−Pipealso improves the distributed$\sf{Skip-Gram}$Skip−Gramlearning model to generate node embeddings by optimizing access locality, CPU throughput, and synchronization efficiency. Finally,$\sf{DistGER-Pipe}$DistGER−Pipedesigns pipelined execution that decouples the operators in sampling and training procedures with an inter-round serial and intra-round parallel processing, attaining optimal utilization of computing resources. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including$\sf{KnightKing}$KnightKing,$\sf{DistDGL}$DistDGL,$\sf{Pytorch-BigGraph}$Pytorch−BigGraph, and$\sf{DistGER}$DistGER,$\sf{DistGER-Pipe}$DistGER−Pipeexhibits 3.15×–1053× acceleration, 45% reduction in cross-machines communication, >10% effectiveness improvement in downstream tasks, and 38% enhancement in CPU utilization.

Key- Value Stores (KVS) implemented with log- structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV- separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB).

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33×--129× acceleration, 45% reduction in cross-machines communication, and >10% effectiveness improvement in downstream tasks.

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE leverages hybrid-property heuristic random walk to capture node features, which considers both node degree and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE achieves both efficiency and performance advantages over recent popular graph embedding approaches. For link prediction and multi-label classification, our approach not only offers >10% average gains, but also exhibits 22×-126× speedup compared with existing sampling-based methods.

Reservoir sampling is widely employed to characterize connectivity of large graph streams by producing edge samples. However, existing reservoir-based sampling methods mainly characterize large graph streams by a measure of counting triangles but perform poorly in accuracy when used to analyze the topological characteristics reflected by node degrees because they produce disconnected edge samples, making them ineffective in many applications that require both types of connectivity estimation simultaneously in real time. This paper proposes a new method, called triangle-induced reservoir sampling, or T-Sample, to produce connected edge samples. While every edge in a graph stream is still processed only once by T-Sample, a dual sampling mechanism performing both uniform sampling and non-uniform sampling is carefully designed with a base reservoir and an incremental reservoir. Specifically, the uniform sampling can be used to count triangles by employing the existing algorithms while the non-uniform sampling ensures that the edge samples are connected. Experimental results driven by real datasets show that T-Sample can obtain much more accurate estimations on the distributions of node degrees than the existing reservoir-based sampling methods.

Data reduction has become increasingly important in storage systems due to the explosive growth of digital data in the world that has ushered in the big data era. In this paper, we present DARE, a Deduplication-Aware Resemblance detection and Elimination scheme for compressing backup datasets that effectively combines data deduplication and delta compression to achieve high data reduction efficiency at low overhead. The main idea behind DARE is to employ a scheme, call Duplicate-Adjacency based Resemblance Detection (DupAdj), by considering any two data chunks to be similar (i.e., candidates for delta compression) if their respective adjacent data chunks are found to be duplicate in a deduplication system, and then further enhance the resemblance detection efficiency by an improved super-feature approach. Our experimental results based on real-world and synthetic backup datasets show that DARE achieves an additional data reduction by a factor of more than 2 (2X) on top of deduplication with very low overhead while nearly doubling the data restore performance of deduplication-only systems by supplementing delta compression to deduplication.

Efficient provenance storage is an essential step towards the adoption of provenance. In this paper, we analyze the provenance collected from multiple workloads with a view towards efficient storage. Based on our analysis, we characterize the properties of provenance with respect to long term storage. We then propose a hybrid scheme that takes advantage of the graph structure of provenance data and the inherent duplication in provenance data. Our evaluation indicates that our hybrid scheme, a combination of web graph compression (adapted for provenance) and dictionary encoding, provides the best tradeoff in terms of compression ratio, compression time and query performance when compared to other compression schemes.

The method of finding high-quality answers has significant impact on user satisfaction in community question answering systems. However, due to the lexical gap between questions and answers as well as spam typically existing in usergenerated content, filtering and ranking answers is very challenging. Previous solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Moreover, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of latent links, i.e. positive links indicating high-quality answers, negative links indicating incorrect answers or user-generated spam, and propose an analogical reasoning-based approach which measures the analogy between the new question-answer linkages and those of previous relevant knowledge which contains only positive links; the candidate answer which has the most analogous link is assumed to be the best answer. We conducted experiments based on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of our approach.

Due to the lexical gap between questions and answers, automatically detecting right answers becomes very challenging for community question-answering sites. In this paper, we propose an analogical reasoning-based method. It treats questions and answers as relational data and ranks an answer by measuring the analogy of its link to a query with the links embedded in previous relevant knowledge; the answer that links in the most analogous way to the new question is assumed to be the best answer. We based our experiments on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of the approach.