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Register a new userSpec-QP: Speculative Query Planning for Joins over Knowledge Graphs
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Added by
Madhulika Mohanty
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Organisations store huge amounts of data from multiple heterogeneous sources in the form of Knowledge Graphs (KGs). One of the ways to query these KGs is to use SPARQL queries over a database engine. Since SPARQL follows exact match semantics, the queries may return too few or no results. Recent works have proposed query relaxation where the query engine judiciously replaces a query predicate with similar predicates using weighted relaxation rules mined from the KG. The space of possible relaxations is potentially too large to fully explore and users are typically interested in only top-k results, so such query engines use top-k algorithms for query processing. However, they may still process all the relaxations, many of whose answers do not contribute towards top-k answers. This leads to computation overheads and delayed response times. We propose Spec-QP, a query planning framework that speculatively determines which relaxations will have their results in the top-k answers. Only these relaxations are processed using the top-k operators. We, therefore, reduce the computation overheads and achieve faster response times without adversely affecting the quality of results. We tested Spec-QP over two datasets - XKG and Twitter, to demonstrate the efficiency of our planning framework at reducing runtimes with reasonable accuracy for query engines supporting relaxations.
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We study the hardness of Approximate Query Processing (AQP) of various types of queries involving joins over multiple tables of possibly different sizes. In the case where the query result is a single value (e.g., COUNT, SUM, and COUNT(DISTINCT)), we prove worst-case information-theoretic lower bounds for AQP problems that are given parameters $epsilon$ and $delta$, and return estimated results within a factor of 1+$epsilon$ of the true results with error probability at most $delta$. In particular, the lower bounds for cardinality estimation over joins under various settings are contained in our results. Informally, our results show that for various database queries with joins, unless restricted to the set of queries whose results are always guaranteed to be above a very large threshold, the amount of information an AQP algorithm needs for returning an accurate approximation is at least linear in the number of rows in the largest table. Similar lower bounds even hold for some special cases where additional information such as top-K heavy hitters and all frequency vectors are available. In the case of GROUP-BY where the query result is not a single number, we study the lower bound for the amount of information used by any approximation algorithm that does not report any non-existing group and does not miss groups of large total size. Our work extends the work of Alon, Gibbons, Matias, and Szegedy [AGMS99].We compare our lower bounds with the amount of information required by Bernoulli sampling to give an accurate approximation. For COUNT queries with joins over multiple tables of the same size, the upper bound matches the lower bound, unless the problem setting is restricted to the set of queries whose results are always guaranteed to be above a very large threshold.
Knowledge graphs (KG) that model the relationships between entities as labeled edges (or facts) in a graph are mostly constructed using a suite of automated extractors, thereby inherently leading to uncertainty in the extracted facts. Modeling the uncertainty as probabilistic confidence scores results in a probabilistic knowledge graph. Graph queries over such probabilistic KGs require answer computation along with the computation of those result probabilities, aka, probabilistic inference. We propose a system, HAPPI (How Provenance of Probabilistic Inference), to handle such query processing. Complying with the standard provenance semiring model, we propose a novel commutative semiring to symbolically compute the probability of the result of a query. These provenance-polynomiallike symbolic expressions encode fine-grained information about the probability computation process. We leverage this encoding to efficiently compute as well as maintain the probability of results as the underlying KG changes. Focusing on a popular class of conjunctive basic graph pattern queries on the KG, we compare the performance of HAPPI against a possible-world model of computation and a knowledge compilation tool over two large datasets. We also propose an adaptive system that leverages the strengths of both HAPPI and compilation based techniques. Since existing systems for probabilistic databases mostly focus on query computation, they default to re-computation when facts in the KG are updated. HAPPI, on the other hand, does not just perform probabilistic inference and maintain their provenance, but also provides a mechanism to incrementally maintain them as the KG changes. We extend this maintainability as part of our proposed adaptive system.
Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.
As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) have been employed to process KGs. This paper aims to provide an in-depth analysis of query performance across diverse DMSs and KG query types. Our aim is to provide a fine-grained, comparative analysis of four major DMS types, namely, row-, column-, graph-, and document-stores, against major query types, namely, subject-subject, subject-object, tree-like, and optional joins. In particular, we analyzed the performance of row-store Virtuoso, column-store Virtuoso, Blazegraph (i.e., graph-store), and MongoDB (i.e., document-store) using five well-known benchmarks, namely, BSBM, WatDiv, FishMark, BowlognaBench, and BioBench-Allie. Our results show that no single DMS displays superior query performance across the four query types. In particular, row- and column-store Virtuoso are a factor of 3-8 faster for tree-like joins, Blazegraph performs around one order of magnitude faster for subject-object joins, and MongoDB performs over one order of magnitude faster for high-selective queries.
Wireless sensor networks become integral part of our life. These networks can be used for monitoring the data in various domain due to their flexibility and functionality. Query processing and optimization in the WSN is a very challenging task because of their energy and memory constraint. In this paper, first our focus is to review the different approaches that have significant impacts on the development of query processing techniques for WSN. Finally, we aim to illustrate the existing approach in popular query processing engines with future research challenges in query optimization.
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