Matthew Damigos

Query containment under bag and bag-set semantics

Conjunctive queries (CQs) are at the core of query languages encountered in many logic-based research fields such as AI, or database systems. The majority of existing work assumes set semantics but often in real applications the manipulation of duplicate tuples is required. One of the major problems that arises as part of advanced features of query optimization, data integration, query reformulation and many other research topics is testing for containment of such queries. In this work, we investigate the complexity of query containment problem for CQs under bag semantics (i.e. duplicate tuples are allowed in both the database and the results of queries) and under bag-set semantics (i.e. duplicates are allowed in the result of the queries but not in the database). We derive complexity results for these problems for five major subclasses of CQs; and we also find necessary conditions for CQ query containment. The general case of these problems remains open.

Distributed Processing of XPath Queries Using MapReduce

In this paper we investigate the problem of efficiently evaluating XPath queries over large XML data stored in a distributed manner. We propose a MapReduce algorithm based on a query decomposition which computes all expected answers in one MapReduce step. The algorithm can be applied over large XML data which is given either as a single distributed document or as a collection of small XML documents.

An Algorithm for Querying Linked Data Using Map-Reduce

In this paper, we exploit the widely used Map-Reduce framework and propose a generic two-phase, Map-Reduce algorithm for querying large amount of linked data. The algorithm is based on the idea that the data graph can be arbitrarily partitioned into graph segments which can be stored in different nodes of a cluster of commodity computers. To answer a user query Q, Q is also decomposed into a set of subqueries. In the first phase, the subqueries are applied to each graph segment, in isolation, and intermediate results are computed. The intermediate results are combined in the second phase to obtain the answers of the query Q. The proposed algorithm computes the answers to a given query correctly, independently of a) the data graph partitioning, b) how graph segments are stored, c) the query decomposition, and d) the algorithm used for calculating (partial) results.

Distributed evaluation of XPath queries over large integrated XML data

XML is a widespread, text-based format used for exchanging information on the Web and representing metadata. Since the amount of XML information is rapidly increasing, efficient querying of large data repositories, containing XML data, is a significant challenge faced by system designers and data analysts who need to support operational actions and decision-making. In this paper we propose a technique for integrating large amount of XML data and use the Map-Reduce framework to efficiently query the integrated data. Each XML document obtained from the sources is transformed properly in order to fit into a predefined, virtual XML structure. Although the transformed documents are not physically integrated, the user is able to pose queries over a single XML structure. To achieve this feature we propose a single-step, Map-Reduce algorithm which takes advantage of virtual structure and computes efficiently the answer of a given XPath queries in a distributed manner.

Union rewritings for XPath fragments

In this paper, we study the problem of finding an equivalent rewriting of an XPath query using multiple views, and we show that the union operator may be required in order to find such a rewriting. In particular, focusing on the fragment of XPath containing both descendant edges and wildcard labels, we propose an algorithm that outputs a union of single-view rewritings (if there exists any) which equivalently rewrites a given query. For the same fragment of XPath, we give necessary and sufficient conditions for query containment and equivalence of unions of queries.

On solving efficiently the view selection problem under bag and bag-set semantics

In this paper, we investigate the problem of view selection for workloads of conjunctive queries under bag and bag-set semantics. In particular, for both semantics we aim to limit the search space of candidate viewsets. We also start delineating boundaries between query workloads for which certain even more restricted search spaces suffice. They suffice in the sense that they do not compromise optimality in that they contain at least one of the optimal solutions. We start with the general case for both bag and bag-set semantics, where we give a tight condition that candidate views can satisfy and still the search space (thus limited) does contain at least one optimal solution. We show that these results, for both semantics, reduce the size of the search space significantly. Further on, due to this analysis for both semantics, a delineation of the space of viewsets and the space of the corresponding equivalent rewritings for a certain conjunctive query workload is given. We show that for chain query workloads under both bag and bag-set semantics, taking only chain views may miss optimal solutions, whereas, if we further limit the queries to be path-queries (i.e., chain queries over a single binary relation), then, under bag semantics, path-views suffice. Concentrating to bag-set semantics, we show that the path-viewsets do not suffice for every path-query workload.

Redundancy in Linked Data Partitioning for Efficient Query Evaluation

The problem of efficient querying large amount of linked data using Map-Reduce is investigated in this paper. The proposed approach is based on the following assumptions: a) Data graphs are arbitrarily partitioned in the distributed file system is such a way that replication of data triples between the data segments is allowed. b) Data triples are replicated is such a way that answers to a special form of queries, called subject-object star queries, can be obtained from a single data segment. c) Each query posed by the user, can be transformed into a set of subject-object star sub queries. We propose a one and a half phase, scalable, Map-Reduce algorithm that efficiently computes the answers of the initial query by computing and appropriately combining the sub query answers. We prove that, under certain conditions, query can be answered in a single map-reduce phase.

On Solving Efficiently the View Selection Problem under Bag-Semantics

In this paper, we investigate the problem of view selection for workloads of conjunctive queries under bag semantics. In particular we aim to limit the search space of candidate viewsets. In that respect we start delineating the boundary between query workloads for which certain restricted search spaces suffice. They suffice in the sense that they do not compromise optimality in that they contain at least one of the optimal solutions. We start with the general case, where we give a tight condition that candidate views can satisfy and still the search space (thus limited) does contain at least one optimal solution. Preliminary experiments show that this reduces the size of the search space significantly. Then we study special cases. We show that for chain query workloads, taking only chain views may miss all optimum solutions, whereas, if we further limit the queries to be path queries (i.e., chain queries over a single binary relation), then path views suffice. This last result shows that in the case of path queries, taking query subexpressions suffice.