Archiving graph data over history is demanded in many applications, such as social network and bibliographies. Typically, people are interested in querying temporal graphs. Existing keyword search approaches for graph-structured data are insufficient for querying temporal graphs. This paper initiates the study of supporting keyword-based queries on temporal graphs. We propose a search syntax that is an extension of keyword search, which allows casual users to easily search temporal graphs with temporal predicates and ranking functions. To generate results efficiently, we propose a best path iterator, which finds the "best" paths between two data nodes in each snapshot regarding to three ranking factors. We develop algorithms that efficiently generate top-k query results. Extensive experiments verified the efficiency and effectiveness of our approach.

Archiving graph data over history is demanded in many applications, such as social network studies, collaborative projects, scientific graph databases, and bibliographies. Typically people are interested in querying temporal graphs. Existing keyword search approaches for graph-structured data are insufficient for querying temporal graphs. This paper initiates the study of supporting keyword-based queries on temporal graphs. We propose a search syntax that is a moderate extension of keyword search, which allows casual users to easily search temporal graphs with optional predicates and ranking functions related to timestamps. To generate results efficiently, we first propose a best path iterator, which finds the paths between two data nodes in each snapshot that is the “best” with respect to three ranking factors. It prunes invalid or inferior paths and maximizes shared processing among different snapshots. Then, we develop algorithms that efficiently generate top-k query results. Extensive experiments verified the efficiency and effectiveness of our approach.

The data markets are emerging to address many organizations' need to find more useful external data sets for deeper insights. Existing data markets have two main limitations. First, they either sell a whole data set or some fixed views, but do not allow arbitrary ad-hoc queries. Second, they only sell static data, but not data that are frequently updated. While there exist proposed solutions for selling ad-hoc queries, it is an open question what mechanism should be used to sell ad-hoc queries on dynamic data. In this paper, we study a data market framework that enables the sale or sharing of dynamic data, where each sharing is specified by an ad-hoc query. To keep the shared data up-to-date, the service provider should create a view of the shared data and maintain the view for the data buyer. We provide solutions to two important problems in this framework: how to efficiently maintain the views, and how to fairly determine the cost each sharing incurs for its view to be created and maintained by the service provider. Both problems are challenging since in the first problem, different sharings with the same operations in their plans may reuse these operations, and each sharing plan must be generated online. In the second problem, there are several factors that impact the fairness of a costing function and a straightforward mechanism won't work. We propose an intuitive online algorithm for sharing plan selection, as well as a set of fair costing criteria and an algorithm that maximizes the fairness.

Enabling data sharing among mobile apps hosted in the same cloud infrastructure can provide a competitive advantage to the mobile apps by giving them access to rich information as well as increasing the revenue for the cloud provider. We introduce a costing tool that allows application owners (i.e., consumers) and the cloud service provider to assess the cost of a desired data sharing. The costing tool enables the consumers to effectively explore the cost space by choosing between alternative configurations of varying data qualities, specified by the staleness and the accuracy of the data sharing. In other words, staleness and accuracy requirements on the data sharing are used as levers for controlling costs. These capabilities are implemented in a What-if analysis tool, which has been integrated with a large data-sharing platform. We conducted extensive experiments on the integrated platform with a sharing ecosystem created around Twitter data and show the effectiveness of the results produced by the What-if tool.

There has been a great interest in exploiting the cloud as a platform for database as a service. As with other cloud-based services, database services may enjoy cost efficiency through consolidation: hosting multiple databases within a single physical server. Aggressive consolidation, however, may hurt the service quality, leading to SLA violation penalty, which in turn reduces the total business profit, called SLA profit. In this paper, we consider the problem of tenant placement in the cloud for SLA profit maximization, which, as will be shown in the paper, is strongly NP-hard. We propose SLA profit-aware solutions for database tenant placement based on our model for expected penalty computation for multitenant servers. Specifically, we present two approximation algorithms, which have constant approximation ratios, and we further discuss improving the quality of tenant placement using a dynamic programming algorithm. Extensive experiments based on TPC-W workload verified the performance of the proposed approaches.

Decision support and knowledge discovery systems often compute aggregate values of interesting attributes by processing a huge amount of data in very large databases and/or warehouses. In particular, iceberg query is a special type of aggregation query that computes aggregate values above a user-provided threshold. Usually, only a small number of results will satisfy the threshold constraint. Yet, the results often carry very important and valuable business insights. Because of the small result set, iceberg queries offer many opportunities for deep query optimization. However, most existing iceberg query processing algorithms do not take advantage of the small-result-set property and rely heavily on the tuple-scan-based approach. This incurs intensive disk accesses and computation, resulting in long processing time especially when data size is large. Bitmap index, which builds one bitmap vector for each attribute value, is gaining popularity in both column-oriented and row-oriented databases in recent years. It occupies less space than the raw data and gives opportunities for more efficient query processing. In this paper, we exploited the property of bitmap index and developed a very effective bitmap pruning strategy for processing iceberg queries. Our index-pruning-based approach eliminates the need of scanning and processing the entire data set (table) and thus speeds up the iceberg query processing significantly. Experiments show that our approach is much more efficient than existing algorithms commonly used in row-oriented and column-oriented databases.

Studies show that about 50% of Web search is for information exploration purposes, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potential large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. Interestingly, we find that the metadata information embedded in structured data provides a potential for automating or semi-automating the comparison of multiple results. In this article we present an approach for structured data search result differentiation. We define the differentiability of query results and quantify the degree of difference. Then we define the problem of identifying a limited number of valid features in a result that can maximally differentiate this result from the others, which is proved NP-hard. We propose two local optimality conditions, namely single-swap and multi-swap, and design efficient algorithms to achieve local optimality. We then present a feature type-based approach, which further improves the quality of the features identified for result differentiation. To show the usefulness of our approach, we implemented a system CompareIt, which can be used to compare structured search results as well as any objects. Our empirical evaluation verifies the effectiveness and efficiency of the proposed approach.

Keyword query is a user-friendly mechanism for retrieving useful information from XML data in Web and scientific applications. Inspired by the performance benefits of exploiting materialized views when processing structured queries, we investigate the feasibility and present a general framework for answering XML keyword queries using materialized views. Then we develop an XML keyword search engine that leverages materialized views for query evaluation and maintains materialized views incrementally upon XML data update. Experimental evaluation demonstrates the significance and efficiency of our approach.

Database evolution is the process of updating the schema of a database or data warehouse (schema evolution) and evolving the data to the updated schema (data evolution). It is often desired or necessitated when changes occur to the data or the query workload, the initial schema was not carefully designed, or more knowledge of the database is known and a better schema is concluded. The Wikipedia database, for example, has had more than 170 versions in the past 5 years [8]. Unfortunately, although much research has been done on the schema evolution part, data evolution has long been a prohibitively expensive process, which essentially evolves the data by executing SQL queries and re-constructing indexes. This prevents databases from being flexibly and frequently changed based on the need and forces schema designers, who cannot afford mistakes, to be highly cautious. Techniques that enable efficient data evolution will undoubtedly make life much easier.

Empowering users to access databases using simple keywords can relieve users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast-evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword-based search and exploration on databases. Several topics will be discussed, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, result comparison, query cleaning and suggestion, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. Finally we identify the challenges and opportunities for future research to advance the field.

Query expansion is a functionality of search engines that suggests a set of related queries for a user-issued keyword query. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. Using these approaches, the expanded queries may correspond to a subset of possible query semantics, and thus miss relevant results. To handle ambiguous queries and exploratory queries, whose result relevance is difficult to judge, we propose a new framework for keyword query expansion: we start with clustering the results according to user specified granularity, and then generate expanded queries, such that one expanded query is generated for each cluster whose result set should ideally be the corresponding cluster. We formalize this problem and show its APX-hardness. Then we propose two efficient algorithms named iterative single-keyword refinement and partial elimination based convergence, respectively, which effectively generate a set of expanded queries from clustered results that provides a classification of the original query results. We believe our study of generating an optimal query based on the ground truth of the query results not only has applications in query expansion, but has significance for studying keyword search quality in general.

Workflow views abstract groups of tasks in a workflow into high level composite tasks, in order to reuse subworkflows and facilitate provenance analysis. However, unless a view is carefully designed, it may not preserve the dataflow between tasks in the workflow, that is, it may not be sound . Unsound views can be misleading and cause incorrect provenance analysis. This article studies the problem of efficiently identifying and correcting unsound workflow views with minimal changes, and constructing minimal sound and elucidative workflow views with a set of user-specified relevant tasks. In particular, two related problems are investigated. First, given a workflow view, we wish to split each unsound composite task into the minimal number of tasks, such that the resulting view is sound. Second, given a workflow and a set of user specified relevant tasks, we generate a sound view, such that each composite task contains at most one relevant task, and the total number of tasks is minimized. We prove that both problems are NP-hard by reduction from independent set. We then propose two local optimality conditions (weak and strong) for each problem, and design polynomial time algorithms for both problems to meet these conditions. Experiments show that our proposed algorithms are reasonably effective and efficient. The proposed techniques are useful for view analysis/construction for not only workflows, but general networks as well.

This demo illustrates an XML search engine TargetSearch that addresses an open problem in XML keyword search: given relevant matches to keywords, how to compose query results properly so that they can be effectively ranked and easily digested by users. The approaches adopted in the literature generate either overwhelmingly large results or fragmentary results, both of which may cause the ranking schemes to be ineffective. Intuitively, each query has a search target and each result should contain exactly one instance of the search target along with its evidence. We developed TargetSearch which composes atomic and intact query results driven by users' search targets.

Database evolution is the process of updating the schema of a database or data warehouse (schema evolution) and evolving the data to the updated schema (data evolution). Database evolution is often necessitated in relational databases due to the changes of data or workload, the suboptimal initial schema design, or the availability of new knowledge of the database. It involves two steps: updating the database schema, and evolving the data to the new schema. Despite the capability of commercial RDBMSs to well optimize query processing, evolving the data during a database evolution through SQL queries is shown to be prohibitively costly. We designed and developed CODS, a platform for efficient data level data evolution in column oriented databases, which evolves the data to the new schema without materializing query results or unnecessary compression/decompression as occurred in traditional query level approaches. CODS ameliorates the efficiency of data evolution by orders of magnitude compared with commercial or open source RDBMSs.

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this demo we present a system XSACT for differentiating search results on structured data. XSACT takes as input a set of structured results, and outputs a Differentiation Feature Set (DFS) for each result to highlight their differences within a size bound. The problem of generating DFSs with maximal differences is proved to be NP-hard. XSACT adopts efficient algorithms for DFS generation, and features a user-friendly interface that effectively interacts with the users to help them compare search results.

Workflows are prevalent in diverse applications, which can be scientific experiments, business processes, web services, or recipes. With the dramatically growing number of workflows, there is an increasing need for people to search a workflow repository using keywords and to retrieve the relevant ones. A workflow hierarchy is a three dimensional object containing multiple abstraction views of different granularity on the same workflow. This unique structure poses a new set of challenges compared to keyword search on tree or graph structures typically found in relational or XML data. In this paper, we define an informative, self-contained and concise search result on workflows to be a projection of a workflow hierarchy on a two dimensional viewing plane inferred from user queries. We then design and develop an efficient keyword search engine for workflows. Experimental evaluation demonstrates the effectiveness of our approach.

Keyword search enables Web users to easily access XML data without the need to learn a structured query language and to study possibly complex data schemas. Existing work has addressed the problem of selecting qualified data nodes that match keywords and connecting them in a meaningful way, in the spirit of inferring the where clause in XQuery. However, how to infer the return clause for keyword searches is an open problem. To address this challenge, we present a keyword search engine for data-centric XML, XSeek, to infer the semantics of the search and identify return nodes effectively. XSeek recognizes possible entities and attributes inherently represented in the data. It also distinguishes between predicates and return specifications in query keywords. Then based on the analysis of both XML data structures and keyword patterns, XSeek generates return nodes. Furthermore, when the query is ambiguous and it is hard or impossible to determine the desirable return information, XSeek clusters the query results according to their semantics based on the user-specified granularity, and enables the user to easily browse and select the desired ones. Extensive experimental studies show the effectiveness and efficiency of XSeek.

Snippets are used by almost every text search engine to complement the ranking scheme in order to effectively handle user searches, which are inherently ambiguous and whose relevance semantics are difficult to assess. Despite the fact that XML is a standard representation format of Web data, research on generating result snippets for XML search remains limited. To tackle this important yet open problem, in this article, we present a system eXtract which generates snippets for XML search results. We identify that a good XML result snippet should be a meaningful information unit of a small size that effectively summarizes this query result and differentiates it from others, according to which users can quickly assess the relevance of the query result. We have designed and implemented a novel algorithm to satisfy these requirements. Furthermore, we propose to cluster the query results based on their snippets. Since XML result clustering can only be done at query time, snippet-based clustering significantly improves the efficiency while compromising little clustering accuracy. We verified the efficiency and effectiveness of our approach through experiments.

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.

Workflow views abstract groups of tasks in a workflow into high level composite tasks, in order to reuse sub-workflows and facilitate provenance analysis. However, unless a view is carefully designed, it may not preserve the dataflow between tasks in the workflow, i.e., it may not be sound. Unsound views can be misleading and cause incorrect provenance analysis.

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this paper we present an approach for differentiating search results on structured data. We define the differentiability of query results and quantify the degree of difference. Then we define the problem of identifying a limited number of valid features in a result that can maximally differentiate this result from the others, which is proved to be NP-hard. We propose two local optimality conditions, namely single-swap and multi-swap. Efficient algorithms are designed to achieve local optimality. To show the applicability of our approach, we implemented a system XRed for XML result differentiation. Our empirical evaluation verifies the effectiveness and efficiency of the proposed approach.

Workflow views abstract groups of tasks in a workflow into composite tasks, and are used for simplifying provenance analysis, workflow sharing and reuse. An unsound view does not preserve the dataflow between tasks in the workflow, and can therefore cause incorrect provenance analysis. In this demo we present WOLVES, a system that efficiently identifies and corrects unsound workflow views with minimal changes ( view correction ). Since the view correction problem is NP-hard, WOLVES allows the user to choose between two forms of local optimality, strong and weak. Efficient time algorithms achieving these optimalities are implemented in WOLVES.

Answering queries using materialized views has been well studied in the context of structured queries and has shown significant performance benefits. Despite the popularity of keyword search over XML data, it is an open problem whether materialized views can be leveraged for query evaluation. In this paper, we investigate this problem and present techniques for answering keyword queries using a minimal number of materialized views. Experimental evaluation demonstrates the efficiency of the proposed techniques.

Snippets are used by almost every text search engine to complement ranking scheme in order to effectively handle user searches, which are inherently ambiguous and whose relevance semantics are difficult to assess. Despite the fact that XML is a standard representation format of web data, research on generating result snippets for XML search remains untouched.

Snippets are used by almost every text search engine to complement ranking schemes in order to effectively handle user keyword search. Despite the fact that XML is a standard representation format of web data, research on generating result snippets for XML search remains untouched. In this work, we present eXtract, a system that efficiently generates self-contained result snippets within a given size bound which effectively summarize the query results and differentiate them from one another, according to which users can quickly assess the relevance of the query results.

Keyword search is a user-friendly mechanism for retrieving XML data in web and scientific applications. An intuitively compelling but vaguely defined goal is to identify matches to query keywords that are relevant to the user. However, it is hard to directly evaluate the relevance of query results due to the inherent ambiguity of search semantics. In this work, we investigate an axiomatic framework that includes two intuitive and non-trivial properties that an XML keyword search technique should ideally satisfy: monotonicity and consistency , with respect to data and query. This is the first work that reasons about keyword search strategies from a formal perspective. Then we propose a novel semantics for identifying relevant matches, which, to the best of our knowledge, is the only existing algorithm that satisfies both properties. An efficient algorithm is designed for realizing this semantics. Extensive experimental studies have verified the intuition of the properties and shown the effectiveness of the proposed algorithm.

Keyword search enables web users to easily access XML data without the need to learn a structured query language and to study possibly complex data schemas. Existing work has addressed the problem of selecting qualified data nodes that match keywords and connecting them in a meaningful way, in the spirit of inferring a where clause in XQuery. However, how to infer the return clause for keyword search is an open problem.