Rada Chirkova

Efficiently Querying Large XML Data Repositories: A Survey

Extensible markup language (XML) is emerging as a de facto standard for information exchange among various applications on the World Wide Web. There has been a growing need for developing high-performance techniques to query large XML data repositories efficiently. One important problem in XML query processing is twig pattern matching, that is, finding in an XML data tree D all matches that satisfy a specified twig (or path) query pattern Q. In this survey, we review, classify, and compare major techniques for twig pattern matching. Specifically, we consider two classes of major XML query processing techniques: the relational approach and the native approach. The relational approach directly utilizes existing relational database systems to store and query XML data, which enables the use of all important techniques that have been developed for relational databases, whereas in the native approach, specialized storage and query processing systems tailored for XML data are developed from scratch to further improve XML query performance. As implied by existing work, XML data querying and management are developing in the direction of integrating the relational approach with the native approach, which could result in higher query processing performance and also significantly reduce system reengineering costs.

Dynamic Request Allocation and Scheduling for Context Aware Applications Subject to a Percentile Response Time SLA in a Distributed Cloud

We consider geographically distributed data centers forming a collectively managed cloud computing system, hosting multiple Service Oriented Architecture (SOA) based context aware applications, each subject to Service Level Agreements (SLA). The Service Level Agreements for each context aware application require the response time of a certain percentile of the input requests to be less than a specified value for a profit to be charged by the cloud provider. We present a novel approach of data-oriented dynamic service-request allocation with gi-FIFO scheduling, in each of the geographically distributed data centers, to globally increase the profit charged by the cloud computing system. Our evaluation shows that our dynamic scheme far outperforms the commonly deployed static allocation with either First in First Out (FIFO) or Weighted Round Robin (WRR) scheduling.

Materializing views with minimal size to answer queries

In this paper we study the following problem. Given a database and a set of queries, we want to find, in advance, a set of views that can compute the answers to the queries, such that the size of the viewset (i.e., the amount of space, in bytes, required to store the viewset) is minimal on the given database. This problem is important for many applications such as distributed databases, data warehousing, and data integration. We explore the decidability and complexity of the problem for workloads of conjunctive queries. We show that results differ significantly depending on whether the workload queries have self-joins. If queries can have self-joins, then a disjunctive viewset can be a better solution than any set of conjunctive views. We show that the problem of finding a minimal-size disjunctive viewset is decidable, and give an upper bound on its complexity. If workload queries cannot have self-joins, there is no need to consider disjunctive viewsets, and we show that the problem is in NP. We describe a very compact search space of conjunctive views, which contains all views in at least one optimal disjunctive viewset. We give a dynamic-programming algorithm for finding minimal-size disjunctive viewsets for queries without self-joins, and discuss heuristics to make the algorithm efficient.

Selecting and using views to compute aggregate queries

We consider a workload of aggregate queries and investigate the problem of selecting views that (1) provide equivalent rewritings for all queries, and (2) are optimal, in that the cost of evaluating the query workload is minimized. We consider conjunctive views and rewritings, with or without aggregation; in each rewriting, only one view contributes to computing the aggregated query output. We look at query rewriting using existing views and at view selection. In the query-rewriting prob- lem, we give sufficient and necessary conditions for a rewriting to exist. For view selection, we prove complexity results. Finally, we give algo- rithms for obtaining rewritings and selecting views.

Exact and inexact methods for selecting views and indexes for OLAP performance improvement

In on-line analytical processing (OLAP), precomputing (materializing as views) and indexing auxiliary data aggregations is a common way of reducing query-evaluation time costs for important data-analysis queries. We consider an OLAP view- and index-selection problem stated as an optimization problem, where (i) the inputs include the data-warehouse schema, a set of data-analysis queries of interest, and a storage-limit constraint, and (ii) the output is a set of views and indexes that minimizes the costs of the input queries, subject to the storage limit. While greedy and other heuristic strategies for choosing views or indexes might help to some extent in improving the costs, it is highly nontrivial to arrive at a globally optimum solution, one that reduces the processing costs of typical OLAP queries as much as is theoretically possible. In fact, as observed in [17] and to the best of our knowledge, there is no known approximation algorithm for OLAP view or index selection with nontrivial performance guarantees. In this paper we propose a systematic study of the OLAP view- and index-selection problem. Our specific contributions are as follows: (1) We develop an algorithm that effectively and efficiently prunes the space of potentially beneficial views and indexes when given realistic-size instances of the problem. (2) We provide formal proofs that our pruning algorithm keeps at least one globally optimum solution in the search space, thus the resulting integer-programming model is guaranteed to find an optimal solution. (3) We develop a family of algorithms to further reduce the size of the search space, so that we are able to solve larger problem instances, although we no longer guarantee the global optimality of the resulting solution. (4) Finally, we present an experimental comparison of our proposed approaches with the state-of-the-art approaches of [2, 12]. Our experiments show that our approaches to view and index selection result in high-quality solutions --- in fact, in globally optimum solutions for many realistic-size problem instances. Thus, they compare favorably with the well-known OLAP-centered approach of [12] and provide for a winning combination with the end-to-end framework of [2] for generic view and index selection.

Answering queries using materialized views with minimum size

In this paper, we study the following problem. Given a database and a set of queries, we want to find a set of views that can compute the answers to the queries, such that the amount of space, in bytes, required to store the viewset is minimum on the given database. (We also handle problem instances where the input has a set of database instances, as described by an oracle that returns the sizes of view relations for given view definitions.) This problem is important for applications such as distributed databases, data warehousing, and data integration. We explore the decidability and complexity of the problem for workloads of conjunctive queries. We show that results differ significantly depending on whether the workload queries have self-joins. Further, for queries without self-joins we describe a very compact search space of views, which contains all views in at least one optimal viewset. We present techniques for finding a minimum-size viewset for a single query without self-joins by using the shape of the query and its constraints, and validate the approach by extensive experiments.

Efficient algorithms for exact ranked twig-pattern matching over graphs

Querying large-scale graph-structured data with twig patterns is attracting growing interest. Generally, a twig pattern could have an extremely large, potentially exponential, number of matches in a graph. Retrieving and returning to the user this many answers may both incur high computational overhead and overwhelm the user. In this paper we propose two efficient algorithms, DP-B and DP-P, for retrieving top-ranked twig-pattern matches from large graphs. Our first algorithm, DP-B, is able to retrieve exact top-ranked answer matches from potentially exponentially many matches in time and space linear in the size of our data inputs even in the worst case. Further, beyond the linear-cost result of DP-B, our second algorithm, DP-P, could take far less than linear time and space cost in practice. To the best of our knowledge, our algorithms are the first to have these performance properties. Our experimental results demonstrate the high performance of both algorithms on large datasets. We also analyze and compare the performance trade-off between DP-B and DP-P from the theoretical and practical viewpoints.

Query evaluation using overlapping views: completeness and efficiency

We study the problem of finding efficient equivalent view-based rewritings of relational queries, focusing on query optimization using materialized views under the assumption that base relations cannot contain duplicate tuples. A lot of work in the literature addresses the problems of answering queries using views and query optimization. However, most of it proposes solutions for special cases, such as for conjunctive queries (CQs) or for aggregate queries only. In addition, most of it addresses the problems separately under set or bag-set semantics for query evaluation, and some of it proposes heuristics without formal proofs for completeness or soundness. In this paper we look at the two problems by considering CQ/A queries - that is, both pure conjunctive and aggregate queries, with aggregation functions SUM, COUNT, MIN, and MAX; the DISTINCT keyword in (SQL versions of) our queries is also allowed. We build on past work to provide algorithms that handle this general setting. This is possible because recent results on rewritings of CQ/A queries [1, 8] show that there are sound and complete algorithms based on containment tests of CQs.Our focus is that our algorithms are efficient as well as sound and complete. Besides the contribution we make in putting and addressing the problems in this general setting, we make two additional contributions for bag-set and set semantics. First, we propose efficient sound and complete tests for equivalence of CQ/A queries to rewritings that use overlapping views (the algorithms are complete with respect to the language of rewritings). These results apply not only to query optimization, but to all areas where the goal is to obtain efficient equivalent view-based query rewritings. Second, based on these results we propose two sound algorithms, BDPV and CDPV, that find efficient execution plans for CQ/A queries in terms of materialized views. Both algorithms extend the cost-based query-optimization approach of System R [19]. The efficient sound algorithm BDPV is also complete in some cases, whereas CDPV is sound and complete for all CQ/A queries we consider. We present a study of the completeness-efficiency tradeoff in the algorithms, and provide experimental results that show the viability of our approach and test the limits of query optimization using overlapping views.

Linearly Bounded Reformulations of Conjunctive Databases

Database reformulation is the process of rewriting the data and rules of a deductive database in a functionally equivalent manner. We focus on the problem of automatically reformulating a database in a way that reduces query processing time while satisfying strong storage space constraints. In previous work we have investigated database reformulation for the case of unary databases. In this paper we extend this work to arbitrary arity, while concentrating on databases with conjunctive rules. The main result of the paper is that the database reformulation problem is decidable for conjunctive databases.

On rewriting XPath queries using views

The problem of rewriting a query using a materialized view is studied for a well known fragment of XPath that includes the following three constructs: wildcards, descendant edges and branches. In earlier work, determining the existence of a rewriting was shown to be coNP-hard, but no tight complexity bound was given. While it was argued that Σ3p is an upper bound, the proof was based on results that have recently been refuted. Consequently, the exact complexity (and even decidability) of this basic problem has been unknown, and there have been no practical rewriting algorithms if the query and the view use all the three constructs mentioned above. It is shown that under fairly general conditions, there are only two candidates for rewriting and hence, the problem can be practically solved by two containment tests. In particular, under these conditions, determining the existence of a rewriting is coNP-complete. The proofs utilize various novel techniques for reasoning about XPath patterns. For the general case, the exact complexity remains unknown, but it is shown that the problem is decidable.