Clustering short texts by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors. Unfortunately, for short texts such as search queries, such representations are insufficient to capture the underlying semantics. We propose a method to add more semantic signals to enrich short texts. Furthermore, we introduce a simplified deep learning network constructed by stacked auto-encoders to do semantic hashing. Experiments show that our method significantly improves the understanding of short texts, including text retrieval, classification and other general text-related tasks.

Clustering short texts (such as news titles) by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors (bag-of-word representation). Unfortunately, for short texts such as search queries, tweets, or news titles, such representations are insufficient to capture the underlying semantics. To cluster short texts by their meanings, we propose to add more semantic signals to short texts. Specifically, for each term in a short text, we obtain its concepts and co-occurring terms from a probabilistic knowledge base to enrich the short text. Furthermore, we introduce a simplified deep learning network consisting of a 3-layer stacked auto-encoders for semantic hashing. Comprehensive experiments show that, with more semantic signals, our simplified deep learning model is able to capture the semantics of short texts, which enables a variety of applications including short text retrieval, classification, and general purpose text processing.

Relation extraction from the Web data has attracted a lot of attention in recent years. However, little work has been done when it comes to relation extraction from the enterprise data regardless of the urgent needs to such work in real applications (e.g., E-discovery). One distinct characteristic of the enterprise data (in comparison with the Web data) is its low redundancy. Previous work on relation extraction from the Web data largely relies on the data's high redundancy level and thus cannot be applied to the enterprise data effectively. This paper proposes an unsupervised hybrid framework called REACTOR. REACTOR combines a statistical method, classification, and clustering to identify various types of relations among entities appearing in the enterprise data automatically. Furthermore, the authors explore to apply pronominal anaphora resolution to extract more relations expressed across multiple sentences. They evaluate REACTOR over a real-world enterprise data set from HP that contains over three million pages and the experimental results show the effectiveness of REACTOR.

Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use support (frequency) to measure the popularity of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the completeness of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern dominant if its occupancy value is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both dominant and frequent. Also, we formulate the problem of mining top-k qualified patterns , that is, finding k qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.

Distance join queries have recently been recognized as a particularly useful operation over graph data, since they capture graph similarity in a meaningful way. Consequently, they have been studied extensively in recent years [1], [2]. However, current methods are designed for centralized systems, and rely on the graph embedding for effective pruning and indexing. As graph sizes become very large and graph data must be deployed in the distributed environment, these techniques become impractical. In this work, we propose a solution for efficient parallel processing of distance join queries over distributed large graphs. There have been emerging efforts devoted to managing large graphs in distributed and parallel systems. Programming models like Pregel [3] and iterative computing framework like HaLoop [4] have been proposed to handle queries over distributed graphs. However, they are designed in the perspective of functionality instead of the query efficiency. In this work, we define an optimization problem: combining the iterative join and the graph exploration method to minimize the evaluation time of distance join queries. Without sacrificing a system's scalability, our technique exploits a light-weight vertex centric encoding schema built on a distance-aware partition of the entire graph. Extensive experiments over both real and synthetic large graphs show that, by employing an adaptive query plan generation and scheduling method, we can effectively reduce the redundant message passing and I/O costs. Compared to simply using iterative join or graph exploration method, our solution achieves as many as one order of magnitude of time saving for the query evaluation.

A popular programming paradigm in the cloud, MapReduce is ex- tensively considered and used for big analysis. Unfortu- nately, a great many big applications require capabilities be- yond those originally intended by MapReduce, often burdening de- velopers to write unnatural non-obvious MapReduce programs so as to twist the underlying system to meet the requirements. In this paper, we focus on a class of big applications that in addi- tion to MapReduce's main data source, require selective access to one or many data sources, e.g., various kinds of indices, knowledge bases, external cloud services. We propose to extend MapReduce with EFind, an Efficient and Flexible index access solution, to better support this class of ap- plications. EFind introduces a standard index access interface to MapReduce so that (i) developers can easily and flexibly express index access operations without unnatural code, and (ii) the EFind enhanced MapReduce system can automatically optimize the in- dex access operations. We propose and analyze a number of in- dex access strategies that utilize caching, re-partitioning, and index locality to reduce redundant index accesses. EFind collects index statistics and performs cost-based adaptive optimization to improve index access efficiency. Our experimental results, using both real- world and synthetic data sets, show that EFind chooses execution plans that are optimal or close to optimal, and achieves a factor of 2x-8x improvements compared to an approach that accesses in- dices without optimization.

With the ever increasing quantities of electronic data, there is a growing need to make sense out of the data. Many advanced database applications are beginning to support this need by integrating domain knowledge encoded as ontologies into queries over relational data. However, it is extremely difficult to express queries against graph structured ontology in the relational SQL query language or its extensions. Moreover, semantic queries are usually not precise, especially when data and its related ontology are complicated. Users often only have a vague notion of their information needs and are not able to specify queries precisely. In this paper, we address these challenges by introducing a novel method to support semantic queries in relational databases with ease. Instead of casting ontology into relational form and creating new language constructs to express such queries, we ask the user to provide a small number of examples that satisfy the query she has in mind. Using those examples as seeds, the system infers the exact query automatically, and the user is therefore shielded from the complexity of interfacing with the ontology. Our approach consists of three steps. In the first step, the user provides several examples that satisfy the query. In the second step, we use machine learning techniques to mine the semantics of the query from the given examples and related ontologies. Finally, we apply the query semantics on the data to generate the full query result. We also implement an optional active learning mechanism to find the query semantics accurately and quickly. Our experiments validate the effectiveness of our approach.

To benefit from the Cloud platform's unlimited resources, managing and evaluating huge volume of RDF data in a scalable manner has attracted intensive research efforts recently. Progresses have been made on evaluating SPARQL queries with either high-level declarative programming languages, like Pig [1], or a sequence of sophisticated designed MapReduce jobs, both of which tend to answer the query with multiple join operations. However, due to the simplicity of Cloud storage and the coarse organization of RDF data in existing solutions, multiple join operations easily bring significant I/O and network traffic which can severely degrade the system performance. In this work, we first propose EAGRE, an Entity-Aware Graph compREssion technique to form a new representation of RDF data on Cloud platforms, based on which we propose an I/O efficient strategy to evaluate SPARQL queries as quickly as possible, especially queries with specified solution sequence modifiers, e.g., PROJECTION, ORDER BY, etc. We implement a prototype system and conduct extensive experiments over both real and synthetic datasets on an in-house cluster. The experimental results show that our solution can achieve over an order of magnitude of time saving for the SPARQL query evaluation compared to the state-of-art MapReduce-based solutions.

Recognizing the various aliases of an entity is a critical task for many applications, including Web search, recommendation system, and e-discovery. The goal of this paper is to accurately identify entity aliases, especially the long tail ones in the unstructured data. Our solution GRIAS (abbr. for a Graph-based framework for discovering entity Aliases) is motivated by the entity relationships collected from both the structured and unstructured data. These relationships help to build an entity-relation graph, and the graph-based similarity is calculated between an entity and its alias candidates which are first chosen by our proposed candidate selection method. Extensive experimental results on two real-world datasets demonstrate both the effectiveness and efficiency of the proposed framework.

This study addresses the problem of inferring users' employment affiliation information from social activities. It is motivated by the applications which need to monitoring and analyzing the social activities of the employees from a given company, especially their social tracks related to the work and business. It definitely helps to better understand their needs and opinions towards certain business area, so that the account sales targeting these customers in the given company can adjust the sales strategies accordingly. Specifically, in this task we are given a snapshot of a social network and some labeled social users who are the employees of a given company. Our goal is to identify more users from the same company. We formulate this problem as a task of classifying nodes over a graph, and develop a Supervised Label Propagation model. It naturally incorporates the rich set of features for social activities, models the networking effect by label propagation, and learns the feature weights so that the labels are propagated to the right users. To validate its effectiveness, we show our case studies on identifying the employees of "China Telecom" and "China Unicom" from Sina Weibo. The experimental results show that our method significantly outperforms the compared baseline ones.

Twitter has become an increasingly important source of information, with more than 400 million tweets posted per day. The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base is called tweet entity linking. This task is of practical importance and can facilitate many different tasks, such as personalized recommendation and user interest discovery. The tweet entity linking task is challenging due to the noisy, short, and informal nature of tweets. Previous methods focus on linking entities in Web documents, and largely rely on the context around the entity mention and the topical coherence between entities in the document. However, these methods cannot be effectively applied to the tweet entity linking task due to the insufficient context information contained in a tweet. In this paper, we propose KAURI, a graph-based framework to collectively link all the named entity mentions in all tweets posted by a user via modeling the user's topics of interest. Our assumption is that each user has an underlying topic interest distribution over various named entities. KAURI integrates the intra-tweet local information with the inter-tweet user interest information into a unified graph-based framework. We extensively evaluated the performance of KAURI over manually annotated tweet corpus, and the experimental results show that KAURI significantly outperforms the baseline methods in terms of accuracy, and KAURI is efficient and scales well to tweet stream.

Social media is a platform for people to share and vote content. From the analysis of the social media data we found that users are quite inactive in rating/voting. For example, a user on average only votes 2 out of 100 accessed items. Traditional recommendation methods are mostly based on users' votes and thus can not cope with this situation. Based on the observation that the dwell time on an item may reflect the opinion of a user, we aim to enrich the user-vote matrix by converting the dwell time on items into users' ``pseudo votes'' and then help improve recommendation performance. However, it is challenging to correctly interpret the dwell time since many subjective human factors, e.g. user expectation, sensitivity to various item qualities, reading speed, are involved into the casual behavior of online reading. In psychology, it is assumed that people have choice threshold in decision making. The time spent on making decision reflects the decision maker's threshold. This idea inspires us to develop a View-Voting model, which can estimate how much the user likes the viewed item according to her dwell time, and thus make recommendations even if there is no voting data available. Finally, our experimental evaluation shows that the traditional rate-based recommendation's performance is greatly improved with the support of VV model.

Wikipedia is an important human generated knowledge base containing over 21 million articles organized by millions of categories. In this paper, we exploit Wikipedia for a new task of text mining: Context-aware Concept Categorization. In the task, we focus on categorizing concepts according to their context. We exploit article link feature and category structure in Wikipedia, followed by introducing Wiki3C, an unsupervised and domain independent concept categorization approach based on context. In the approach, we investigate two strategies to select and filter Wikipedia articles for the category representation. Besides, a probabilistic model is employed to compute the semantic relatedness between two concepts in Wikipedia. Experimental evaluation using manually labeled ground truth shows that our proposed Wiki3C can achieve a noticeable improvement over the baselines without considering contextual information.

Due to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an app recommendation service. Different from conventional item recommendation where the user interest is the primary factor, app recommendation also needs to consider factors that invoke a user to replace an old app (if she already has one) with a new app. In this work we propose an Actual- Tempting model that captures such factors in the decision process of mobile app adoption. The model assumes that each owned app has an actual satisfactory value and a new app under consideration has a tempting value. The former stands for the real satisfactory value the owned app brings to the user while the latter represents the estimated value the new app may seemingly have. We argue that the process of app adoption therefore is a contest between the owned apps' actual values and the candidate app's tempting value. Via the extensive experiments we show that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation. Furthermore, the best recommendation performance is achieved when the AT model is combined with them.

This article studies the problem of prominent streak discovery in sequence data. Given a sequence of values, a prominent streak is a long consecutive subsequence consisting of only large (small) values, such as consecutive games of outstanding performance in sports, consecutive hours of heavy network traffic, and consecutive days of frequent mentioning of a person in social media. Prominent streak discovery provides insightful data patterns for data analysis in many real-world applications and is an enabling technique for computational journalism. Given its real-world usefulness and complexity, the research on prominent streaks in sequence data opens a spectrum of challenging problems. A baseline approach to finding prominent streaks is a quadratic algorithm that exhaustively enumerates all possible streaks and performs pairwise streak dominance comparison. For more efficient methods, we make the observation that prominent streaks are in fact skyline points in two dimensions—streak interval length and minimum value in the interval. Our solution thus hinges on the idea to separate the two steps in prominent streak discovery: candidate streak generation and skyline operation over candidate streaks. For candidate generation, we propose the concept of local prominent streak (LPS). We prove that prominent streaks are a subset of LPSs and the number of LPSs is less than the length of a data sequence, in comparison with the quadratic number of candidates produced by the brute-force baseline method. We develop efficient algorithms based on the concept of LPS. The nonlinear local prominent streak (NLPS)-based method considers a superset of LPSs as candidates, and the linear local prominent streak (LLPS)-based method further guarantees to consider only LPSs. The proposed properties and algorithms are also extended for discovering general top- k , multisequence, and multidimensional prominent streaks. The results of experiments using multiple real datasets verified the effectiveness of the proposed methods and showed orders of magnitude performance improvement against the baseline method.

Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Many information needs of enterprise search center around entities. Intuitively, information related to the entities mentioned in the query, such as related entities, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities to improve search quality. Experiment results show that the proposed entity-centric query expansion strategy is more effective to improve the search performance than the state-of-the-art pseudo feedback methods on longer, natural language-like queries with entities.

Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques. This issue naturally consists of two subtasks: (1) for the entity mention whose mapping entity does not exist in the ontology, attach it to the right category in the ontology (i.e., fine-grained named entity classification), and (2) for the entity mention whose mapping entity is contained in the ontology, link it with its mapping real world entity in the ontology (i.e., entity linking). Previous studies only focus on one of the two subtasks and cannot solve this task of populating ontology with named entities integrally. This paper proposes APOLLO, a grAph-based aPproach for pOpuLating ontoLOgy with named entities. APOLLO leverages the rich semantic knowledge embedded in the Wikipedia to resolve this task via random walks on graphs. Meanwhile, APOLLO can be directly applied to either of the two subtasks with minimal revision. We have conducted a thorough experimental study to evaluate the performance of APOLLO. The experimental results show that APOLLO achieves significant accuracy improvement for the task of ontology population with named entities, and outperforms the baseline methods for both subtasks.

Mining interesting patterns from transaction databases has attracted a lot of research interest for more than a decade. Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. In this paper, we introduce a new measure of pattern interestingness, occupancy. The measure of occupancy is motivated by some real-world pattern recommendation applications which require that any interesting pattern X should occupy a large portion of the transactions it appears in. Namely, for any supporting transaction t of pattern X, the number of items in X should be close to the total number of items in t. In these pattern recommendation applications, patterns with higher occupancy may lead to higher recall while patterns with higher frequency lead to higher precision. With the definition of occupancy we call a pattern dominant if its occupancy is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both frequent and dominant. Additionally, we also formulate the problem of mining top-k qualified patterns: finding the qualified patterns with the top-k values of any function (e.g. weighted sum of both occupancy and support).

Statistical information extraction (IE) programs are increasingly used to build real-world IE systems such as Alibaba, CiteSeer, Kylin, and YAGO. Current statistical IE approaches consider the text corpora underlying the extraction program to be static. However, many real-world text corpora are dynamic (documents are inserted, modified, and removed). As the corpus evolves, and IE programs must be applied repeatedly to consecutive corpus snapshots to keep extracted information up to date. Applying IE from scratch to each snapshot may be inefficient: a pair of consecutive snapshots may change very little, but unaware of this, the program must run again from scratch. In this paper, we present CRFlex, a system that efficiently executes such repeated statistical IE, by recycling previous IE results to enable incremental update. As the first step, CRFlex focuses on statistical IE programs which use a leading statistical model, Conditional Random Fields (CRFs). We show how to model properties of the CRF inference algorithms for incremental update and how to exploit them to correctly recycle previous inference results. Then we show how to efficiently capture and store intermediate results of IE programs for subsequent recycling. We find that there is a tradeoff between the I/O cost spent on reading and writing intermediate results, and CPU cost we can save from recycling those intermediate results. Therefore we present a cost-based solution to determine the most efficient recycling approach for any given CRF-based IE program and an evolving corpus. We conduct extensive experiments with CRF-based IE programs for 3 IE tasks over a real-world data set to demonstrate the utility of our approach.

A critical step in bridging the knowledge base with the huge corpus of semi-structured Web list data is to link the entity mentions that appear in the Web lists with the corresponding real world entities in the knowledge base, which we call list linking task. This task can facilitate many different tasks such as knowledge base population, entity search and table annotation. However, the list linking task is challenging because a Web list has almost no textual context, and the only input for this task is a list of entity mentions extracted from the Web pages. In this paper, we propose LIEGE, the first general framework to Link the entities in web lists with the knowledge base to the best of our knowledge. Our assumption is that entities mentioned in a Web list can be any collection of entities that have the same conceptual type that people have in mind. To annotate the list items in a Web list with entities that they likely mention, we leverage the prior probability of an entity being mentioned and the global coherence between the types of entities in the Web list. The interdependence between different entity assignments in a Web list makes the optimization of this list linking problem NP-hard. Accordingly, we propose a practical solution based on the iterative substitution to jointly optimize the identification of the mapping entities for the Web list items. We extensively evaluated the performance of our proposed framework over both manually annotated real Web lists extracted from the Web pages and two public data sets, and the experimental results show that our framework significantly outperforms the baseline method in terms of accuracy.

Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although many existing methods attempt to address this task, most of them can either work only on certain types of Web pages (e.g., news- and blog-like web pages), or perform semi-automatically where extra user efforts are required in adjusting the outputs. The problem of clipping any types of Web pages accurately in a totally automatic way remains pretty much open. To this end in this study we harness the wisdom of the crowds to provide accurate recommendation of informative clips on any given Web pages. Specifically, we leverage the knowledge on how previous users clip similar Web pages, and this knowledge repository can be represented as a transaction database where each transaction contains the clips selected by a user on a certain Web page. Then, we formulate a new pattern mining problem, mining top-1 qualified pattern, on transaction database for this recommendation. Here, the recommendation considers not only the pattern support but also the pattern occupancy (proposed in this work). High support requires that patterns appear frequently in the database, while high occupancy requires that patterns occupy a large portion of the transactions they appear in. Thus, it leads to both precise and complete recommendation. Additionally, we explore the properties on occupancy to further prune the search space for high-efficient pattern mining. Finally, we show the effectiveness of the proposed algorithm on a human-labeled ground truth dataset consisting of 2000 web pages from 100 major Web sites, and demonstrate its efficiency on large synthetic datasets.

Entity aliases commonly exist and accurately detecting these aliases plays a vital role in various applications. In this paper, we use an active-learning-based method to detect aliases without string similarity. To minimize the cost on pairwise comparison, a subset-based method restricts the alias selection within a small-scale entity set. Within each generated entity set, an active learning based logistic regression classifier is employed to predict whether a candidate is the alias of a given entity. The experimental results on three datasets clearly demonstrate that our proposed approach can effectively detect this kind of entity aliases.

Prediction of popular items in online content sharing systems has recently attracted a lot of attention due to the tremendous need of users and its commercial values. Different from previous works that make prediction by fitting a popularity growth model, we tackle this problem by exploiting the latent conforming and maverick personalities of those who vote to assess the quality of on-line items. We argue that the former personality prompts a user to cast her vote conforming to the majority of the service community while on the contrary the later personality makes her vote different from the community. We thus propose a Conformer-Maverick (CM) model to simulate the voting process and use it to rank top-k potentially popular items based on the early votes they received. Through an extensive experimental evaluation, we validate our ideas and find that our proposed CM model achieves better performance than baseline solutions, especially for smaller k.

Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.

Multi-way Theta-join queries are powerful in describing complex relations and therefore widely employed in real practices. However, existing solutions from traditional distributed and parallel databases for multi-way Theta-join queries cannot be easily extended to fit a shared-nothing distributed computing paradigm, which is proven to be able to support OLAP applications over immense data volumes. In this work, we study the problem of efficient processing of multi-way Theta-join queries using MapReduce from a cost-effective perspective. Although there have been some works using the ( key, value ) pair-based programming model to support join operations, efficient processing of multi-way Theta-join queries has never been fully explored. The substantial challenge lies in, given a number of processing units (that can run Map or Reduce tasks), mapping a multi-way Theta-join query to a number of MapReduce jobs and having them executed in a well scheduled sequence, such that the total processing time span is minimized. Our solution mainly includes two parts: 1) cost metrics for both single MapReduce job and a number of MapReduce jobs executed in a certain order; 2) the efficient execution of a chain-typed Theta-join with only one MapReduce job. Comparing with the query evaluation strategy proposed in [23] and the widely adopted Pig Latin and Hive SQL solutions, our method achieves significant improvement of the join processing efficiency.

Search over enterprise data is essential to every aspect of an enterprise because it helps users fulfill their information needs. Similar to Web search, most queries in enterprise search are keyword queries. However, enterprise search is a unique research problem because, compared with the data in traditional IR applications (e.g., text data), enterprise data includes information stored in different formats. In particular, enterprise data include both unstructured and structured information, and all the data center around a particular enterprise. As a result, the relevant information from these two data sources could be complementary to each other. Intuitively, such integrated data could be exploited to improve the enterprise search quality. Despite its importance, this problem has received little attention so far. In this paper, we demonstrate the feasibility of leveraging the integrated information in enterprise data to improve search quality through a case study, i.e., finding relevant information of certain types from enterprise data. Enterprise search users often look for different types of relevant information other than documents, e.g., the contact information of per- sons working on a product. When formulating a keyword query, search users may specify both content requirements, i.e., what kind of information is relevant, and type requirements, i.e., what type of information is relevant. Thus, the goal is to find information relevant to both requirements specified in the query. Specifically, we formulate the problem as keyword search over structured or semistructured data, and then propose to leverage the complementary unstructured information in the enterprise data to solve the problem. Experiment results over real world enterprise data and simulated data show that the proposed methods can effectively exploit the unstructured information to find relevant information of certain types from structured and semistructured information in enterprise data.

With the fast growth of the knowledge bases built over the Internet, storing and querying millions or billions of RDF triples in a knowledge base have attracted increasing research interests. Although the latest RDF storage systems achieve good querying performance, few of them pay much attention to the characteristic of dynamic growth of the knowledge base. In this paper, to consider the efficiency of both querying and incremental update in RDF data, we propose a hAsh-based tWo-tiEr rdf sTOrage system (abbr. to AWETO) with new index architecture and query execution engine. The performance of our system is systematically measured over two large-scale datesets. Compared with the other three state-of-the-art RDF storage systems, our system achieves the best incremental update efficiency, meanwhile, the query efficiency is competitive.

Search result diversification aims to return a list of diversified relevant documents in order to satisfy different user information needs. Most of the efforts focused on Web Search, and few studies have considered another important search domain, i.e., enterprise search. Unlike Web search, enterprise search deals with both unstructured and structured data. In this paper, we propose to integrate the structured and unstructured data to discover meaningful query subtopics in search result diversification. Experimental results show that integrating structured and unstructured information allows us to discover high quality query, which are effective in diversifying the retrieval results.

This paper studies the problem of prominent streak discovery in sequence data. Given a sequence of values, a prominent streak is a long consecutive subsequence consisting of only large (small) values. For finding prominent streaks, we make the observation that prominent streaks are skyline points in two dimensions- streak interval length and minimum value in the interval. Our solution thus hinges upon the idea to separate the two steps in prominent streak discovery' candidate streak generation and skyline operation over candidate streaks. For candidate generation, we propose the concept of local prominent streak (LPS). We prove that prominent streaks are a subset of LPSs and the number of LPSs is less than the length of a data sequence, in comparison with the quadratic number of candidates produced by a brute-force baseline method. We develop efficient algorithms based on the concept of LPS. The non-linear LPS-based method (NLPS) considers a superset of LPSs as candidates, and the linear LPS-based method (LLPS) further guarantees to consider only LPSs. The results of experiments using multiple real datasets verified the effectiveness of the proposed methods and showed orders of magnitude performance improvement against the baseline method.

Aggregate similarity search, a.k.a. aggregate nearest neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves the most (or top-k) similar object to Q from a database P, where the similarity is an aggregation (e.g., sum, max) of the distances between the retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of ÆM objects in Q for some support 0 < Æ d 1. We call this new definition flexible aggregate similarity (Fann) search, which generalizes the Ann problem. Next, we present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return nearoptimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality.

The problem of answering SPARQL queries over virtual SPARQL views is commonly encountered in a number of settings, including while enforcing security policies to access RDF data, or when integrating RDF data from disparate sources. We approach this problem by rewriting SPARQL queries over the views to equivalent queries over the underlying RDF data, thus avoiding the costs entailed by view materialization and maintenance. We show that SPARQL query rewriting combines the most challenging aspects of rewriting for the relational and XML cases: like the relational case, SPARQL query rewriting requires synthesizing multiple views; like the XML case, the size of the rewritten query is exponential to the size of the query and the views. In this paper, we present the first native query rewriting algorithm for SPARQL. For an input SPARQL query over a set of virtual SPARQL views, the rewritten query resembles a union of conjunctive queries and can be of exponential size. We propose optimizations over the basic rewriting algorithm to (i) minimize each conjunctive query in the union; (ii) eliminate conjunctive queries with empty results from evaluation; and (iii) efficiently prune out big portions of the search space of empty rewritings. The experiments, performed on two RDF stores, show that our algorithms are scalable and independent of the underlying RDF stores. Furthermore, our optimizations have order of magnitude improvements over the basic rewriting algorithm in both the rewriting size and evaluation time.

Many dynamic applications are built upon large network infrastructures, such as social networks, communication networks, biological networks and the Web. Such applications create data that can be naturally modeled as graph streams , in which edges of the underlying graph are received and updated sequentially in a form of a stream. It is often necessary and important to summarize the behavior of graph streams in order to enable effective query processing. However, the sheer size and dynamic nature of graph streams present an enormous challenge to existing graph management techniques. In this paper, we propose a new graph sketch method, gSketch, which combines well studied synopses for traditional data streams with a sketch partitioning technique, to estimate and optimize the responses to basic queries on graph streams. We consider two different scenarios for query estimation: (1) A graph stream sample is available; (2) Both a graph stream sample and a query workload sample are available. Algorithms for different scenarios are designed respectively by partitioning a global sketch to a group of localized sketches in order to optimize the query estimation accuracy. We perform extensive experimental studies on both real and synthetic data sets and demonstrate the power and robustness of gSketch in comparison with the state-of-the-art global sketch method.

An increasing number of applications operate on data obtained from the Web. These applications typically maintain local copies of the web data to avoid network latency in data accesses. As the data on the Web evolves, it is critical that the local copy be kept up-to-date. Data freshness is one of the most important data quality issues, and has been extensively studied for various applications including web crawling. However, web crawling is focused on obtaining as many raw web pages as possible. Our applications, on the other hand, are interested in specific content from specific data sources. Knowing the content or the semantics of the data enables us to differentiate data items based on their importance and volatility, which are key factors that impact the design of the data synchronization strategy. In this work, we formulate the concept of content freshness, and present a novel approach that maintains content freshness with least amount of web communication. Specifically, we assume data is accessible through a general keyword search interface, and we form keyword queries based on their selectivity, as well their contribution to content freshness of the local copy. Experiments show the effectiveness of our approach compared with several naive methods for keeping data fresh.

Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted the paramount importance of establishing semantic links among web data sources. Currently, LOD sources provide billions of RDF triples, but only millions of links between data sources. Many of these data sources are published using tools that operate over relational data stored in a standard RDBMS. In this paper, we present a framework for discovery of semantic links from relational data. Our framework is based on declarative specification of linkage requirements by a user. We illustrate the use of our framework using several link discovery algorithms on a real world scenario. Our framework allows data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.

While provenance has been extensively studied in the literature, the efficient evaluation of provenance queries remains an open problem. Traditional query optimization techniques, like the use of general-purpose indexes, or the materialization of provenance data, fail on different fronts to address the problem. Therefore, the need to develop provenance-aware access methods becomes apparent. This paper starts by identifying some key requirements that are to a large extent specific to provenance queries and are necessary for their efficient evaluation. The first such property, called duality, requires that a single access method is used to evaluate both backward provenance queries (which input items of some analysis generate an output item) and forward provenance queries (which outputs of some analysis does an input item generate). The second property, called locality, guarantees that provenance query evaluation times should depend mainly on the size of the provenance query results and should be largely independent of the total size of provenance data. Motivated by the above, we identify proper data structures with the aforementioned properties, we implement them, and through a detailed set of experiments, we illustrate their effectiveness on the evaluation of provenance queries.

Supporting semantic queries in relational databases is essential to many advanced applications. Recently, with the increasing use of ontology in various applications, the need for querying relational data together with its related ontology has become more urgent. In this paper, we identify and discuss the problem of querying relational data with its ontologies. Two fundamental challenges make the problem interesting. First, it is extremely difficult to express queries against graph structured ontology in the relational query language SQL, and second, in many cases where data and its related ontology are complicated, queries are usually not precise, that is, users often have only a vague notion, rather than a clear understanding and definition, of what they query for. We outline a query-by-example approach that enables us to support semantic queries in relational databases with ease. Instead of endeavoring to incorporate ontology into relational form and create new language constructs to express such queries, we ask the user to provide a small number of examples that satisfy the query she has in mind. Using these examples as seeds, the system infers the exact query automatically, and the user is therefore shielded from the complexity of interfacing with the ontology.

While models for data provenance have been extensively studied in the literature, the efficient evaluation of the resulting provenance queries remains an open problem. Traditional query optimization techniques, like the use of general-purpose indexes, or the materialization of provenance data, fail on different fronts to address the problem. Provenance-specific optimization techniques, like the use of customized indexes, similarly prove inadequate since the techniques are bound to specific provenance models. Therefore, the need to develop generic provenance-aware techniques quickly becomes apparent. In this paper, we argue for such a generic technique in the form of a provenance index structure that can be used to efficiently evaluate provenance queries in a variety of contexts. By highlighting the limitations of existing techniques, we identify the set of key properties of the generic index, including a novel property called duality which guarantees that the single index can evaluate both backward provenance queries (which data items from a set I are associated with an item from set O) and forward provenance queries (which items from O are associated with an item from I).

In this paper, we present a framework for online discovery of semantic links from relational data. Our framework is based on declarative specification of the linkage requirements by the user, that allows matching data items in many real-world scenarios. These requirements are translated to queries that can run over the relational data source, potentially using the semantic knowledge to enhance the accuracy of link discovery. Our framework lets data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.

We present Linkage Query Writer (LinQuer), a system for generating SQL queries for semantic link discovery over relational data. The LinQuer framework consists of (a) LinQL, a language for specification of linkage requirements; (b) a web interface and an API for translating LinQL queries to standard SQL queries; (c) an interface that assists users in writing LinQL queries. We discuss the challenges involved in the design and implementation of a declarative and easy to use framework for discovering links between different data items in a single data source or across different data sources. We demonstrate different steps of the linkage requirements specification and discovery process in several real world scenarios and show how the LinQuer system can be used to create high-quality linked data sources.

Relational database management systems are constantly being extended and augmented to accommodate data in different domains. Recently, with the increasing use of ontology in various applications, the need to support ontology, especially the related inferencing operation, in DBMS has become more concrete and urgent. However, manipulating knowledge along with relational data in DBMSs is not a trivial undertaking due to the mismatch in data models. In this paper, we introduce a framework for managing relational data and hierarchical domain knowledge together. Our framework persists taxonomies contained in ontologies by leveraging XML support in hybrid relational-XML DBMSs (e.g., IBM's DB2 v9) and rewrites ontology-based semantic matching queries using the industry-standard query languages, SQL/XML and XQuery. Compared with previous approaches, our approach does not materialize transitive closures of ontological relationships to support inferencing. Consequently, our method has wide applicability and good performance.

Remote health monitoring affords the possibility of improving the quality of health care by enabling relatively inexpensive out-patient care. However, remote health monitoring raises new a problem: the potential for data explosion in health care systems. To address this problem, the remote health monitoring systems must be integrated with analysis tools that provide automated trend analysis and event detection in real time. In this paper, we propose an overview of Century, an extensible framework for analysis of large numbers of remote sensor-based medical data streams.

The Boolean semantics of SQL queries cannot adequately capture the "fuzzy" preferences and "soft" criteria required in non-traditional data retrieval applications. One way to solve this problem is to add a flavor of "information retrieval" into database queries by allowing fuzzy query conditions and flexibly supporting grouping and ranking of the query results within the DBMS engine. While ranking is already supported by all major commercial DBMSs natively, support of flexibly grouping is still very limited (i.e., group-by).

The wide spread of databases for managing structured data, compounded with the expanded reach of the Internet, has brought forward interesting data retrieval and analysis scenarios to RDBMS. In such settings, queries often take the form of k-constrained optimization, with a Boolean constraint and a numeric optimization expression as the goal function, retrieving only the top-k tuples. This paper proposes the concept of supporting such queries, as their nature implies, by a functional optimization machinery over the search space of multiple indices. To realize this concept, we combine the dual perspectives of discrete state search (from the view of indices) and continuous function optimization (from the view of goal functions). We present, as the marriage of the two perspectives, the OPT* framework, which encodes k-constrained optimization as an A* search over the composite space of multiple indices, driven by functional optimization for providing tight heuristics. By processing queries as optimization, OPT* significantly outperforms baseline approaches, with up to 3 orders of magnitude margins.

Health activity monitoring (HAM) has received increasing attention due to the rapid advances of both hardware and software technologies and strong environmental and public health needs. In this paper, we describe the architecture and implementation of the Epi-SPIRE prototype, which is a novel health activity monitoring system that generates alerts from environmental, behavioral, and public health data sources. A model-based approach is used to develop disease and behavior models from multi-modal heterogeneous data sources. Furthermore, a model-based indexing technique has been developed to speed up the data access and retrieval. This system has been successfully applied to various genuine and simulated diseases outbreaks scenarios'.

Recent work on incremental crawling has enabled the indexed document collection of a search engine to be more synchronized with the changing World Wide Web. However, this synchronized collection is not immediately searchable, because the keyword index is rebuilt from scratch less frequently than the collection can be refreshed. An inverted index is usually used to index documents crawled from the web. Complete index rebuild at high frequency is expensive. Previous work on incremental inverted index updates have been restricted to adding and removing documents. Updating the inverted index for previously indexed documents that have changed has not been addressed.In this paper, we propose an efficient method to update the inverted index for previously indexed documents whose contents have changed. Our method uses the idea of landmarks together with the diff algorithm to significantly reduce the number of postings in the inverted index that need to be updated. Our experiments verify that our landmark-diff method results in significant savings in the number of update operations on the inverted index.

Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, approximate answers to OLAP aggregation queries.

Article Free Access Share on Data cube approximation and histograms via wavelets Authors: Jeffrey Scott Vitter Center for Geometric Computing and Department of Computer Science, Duke University, Durham, NC Center for Geometric Computing and Department of Computer Science, Duke University, Durham, NCView Profile , Min Wang Center for Geometric Computing and Department of Computer Science, Duke University, Durham, NC Center for Geometric Computing and Department of Computer Science, Duke University, Durham, NCView Profile , Bala Iyer Database Technology Institute, IBM Santa Teresa Laboratory, P.O. Box 49023, San Jose, CA Database Technology Institute, IBM Santa Teresa Laboratory, P.O. Box 49023, San Jose, CAView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 96–104https://doi.org/10.1145/288627.288645Online:01 November 1998Publication History 169citation593DownloadsMetricsTotal Citations169Total Downloads593Last 12 Months34Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Classification is a key function of many business intelligence toolkits and a fundamental building block in data mining. Immense data may be needed to train a classifier for good accuracy. The state-of-art classifiers need an in-memory data structure of size O(N), where N is the size of the training data, to achieve efficiency. For large data sets, such a data structure will not fit in the internal memory. The best previously known classifier does a quadratic number of I/Os for large N. We propose a novel classification algorithm (classifier) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classifier to be built into a relational database system directly. MIND is truly scalable with respect to I/O efficiency, which is important since scalability is a key requirement for any data mining algorithm. We built a prototype of MIND in the relational database manager DB2 and benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of the datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond the dataset sizes previously published for classifiers. We also give some insights that may have an impact on the evolution of the extended relational calculus SQL.

Query optimization is an integral part of relational database management systems. One important task in query optimization is selectivity estimation. Given a query P, we need to estimate the fraction of records in the database that satisfy P. Many commercial database systems maintain histograms to approximate the frequency distribution of values in the attributes of relations. In this paper, we present a technique based upon a multiresolution wavelet decomposition for building histograms on the underlying data distributions. Histograms built on the cumulative data distributions give very good approximations with limited space usage. We give fast algorithms for constructing histograms and using them in an on-line fashion for selectivity estimation. Our histograms can also be used to provide quick approximate answers to OLAP queries when the exact answers are not required. Our method captures the joint distribution of multiple attributes effectively, especially when the attributes are correlated. Experiments confirm that our histograms offer substantial improvements in accuracy over random sampling and other previous approaches.

Query optimization is an integral part of relational database management systems. One important task in query optimization is selectivity estimation, that is, given a query P, one needs to estimate the fraction of records in the database that satisfy P. Almost all previous work dealt with the estimation of numeric selectivity, i.e., the query contains only numeric variables. The general problem of estimating alphanumeric selectivity is much more difficult and has attracted attention only very recently, and the focus has been on the special case when only one column is involved. The authors consider the more general case when there are two correlated alphanumeric columns. They develop efficient algorithms to build storage structures that can fit in a database catalog. Results from extensive experiments to test the algorithms, on the basis of error analysis and space requirements, are given to guide DBMS implementors.