783 research outputs found
Distributed Information Retrieval using Keyword Auctions
This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions
Robust query processing for linked data fragments
Linked Data Fragments (LDFs) refer to interfaces that allow for publishing and querying Knowledge Graphs on the Web. These interfaces primarily differ in their expressivity and allow for exploring different trade-offs when balancing the workload between clients and servers in decentralized SPARQL query processing. To devise efficient query plans, clients typically rely on heuristics that leverage the metadata provided by the LDF interface, since obtaining fine-grained statistics from remote sources is a challenging task. However, these heuristics are prone to potential estimation errors based on the metadata which can lead to inefficient query executions with a high number of requests, large amounts of data transferred, and, consequently, excessive execution times. In this work, we investigate robust query processing techniques for Linked Data Fragment clients to address these challenges. We first focus on robust plan selection by proposing CROP, a query plan optimizer that explores the cost and robustness of alternative query plans. Then, we address robust query execution by proposing a new class of adaptive operators: Polymorphic Join Operators. These operators adapt their join strategy in response to possible cardinality estimation errors. The results of our first experimental study show that CROP outperforms state-of-the-art clients by exploring alternative plans based on their cost and robustness. In our second experimental study, we investigate how different planning approaches can benefit from polymorphic join operators and find that they enable more efficient query execution in the majority of cases
Querying Schemas With Access Restrictions
We study verification of systems whose transitions consist of accesses to a
Web-based data-source. An access is a lookup on a relation within a relational
database, fixing values for a set of positions in the relation. For example, a
transition can represent access to a Web form, where the user is restricted to
filling in values for a particular set of fields. We look at verifying
properties of a schema describing the possible accesses of such a system. We
present a language where one can describe the properties of an access path, and
also specify additional restrictions on accesses that are enforced by the
schema. Our main property language, AccLTL, is based on a first-order extension
of linear-time temporal logic, interpreting access paths as sequences of
relational structures. We also present a lower-level automaton model,
Aautomata, which AccLTL specifications can compile into. We show that AccLTL
and A-automata can express static analysis problems related to "querying with
limited access patterns" that have been studied in the database literature in
the past, such as whether an access is relevant to answering a query, and
whether two queries are equivalent in the accessible data they can return. We
prove decidability and complexity results for several restrictions and variants
of AccLTL, and explain which properties of paths can be expressed in each
restriction.Comment: VLDB201
SQPR: Stream Query Planning with Reuse
When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions. In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads
Non-invasive progressive optimization for in-memory databases
Progressive optimization introduces robustness for database workloads against wrong estimates, skewed data, correlated attributes, or outdated statistics. Previous work focuses on cardinality estimates and rely on expensive counting methods as well as complex learning algorithms. In this paper, we utilize performance counters to drive progressive optimization during query execution. The main advantages are that performance counters introduce virtually no costs on modern CPUs and their usage enables a non-invasive monitoring. We present fine-grained cost models to detect differences between estimates and actual costs which enables us to kick-start reoptimization. Based on our cost models, we implement an optimization approach that estimates the individual selectivities of a multi-selection query efficiently. Furthermore, we are able to learn properties like sortedness, skew, or correlation during run-time. In our evaluation we show, that the overhead of our approach is negligible, while performance improvements are convincing. Using progressive optimization, we improve runtime up to a factor of three compared to average run-times and up to a factor of 4,5 compared to worst case run-times. As a result, we avoid costly operator execution orders and; thus, making query execution highly robust
Incremental Processing and Optimization of Update Streams
Over the recent years, we have seen an increasing number of applications in networking, sensor networks, cloud computing, and environmental monitoring, which monitor, plan, control, and make decisions over data streams from multiple sources. We are interested in extending traditional stream processing techniques to meet the new challenges of these applications. Generally, in order to support genuine continuous query optimization and processing over data streams, we need to systematically understand how to address incremental optimization and processing of update streams for a rich class of queries commonly used in the applications.
Our general thesis is that efficient incremental processing and re-optimization of update streams can be achieved by various incremental view maintenance techniques if we cast the problems as incremental view maintenance problems over data streams. We focus on two incremental processing of update streams challenges currently not addressed in existing work on stream query processing: incremental processing of transitive closure queries over data streams, and incremental re-optimization of queries. In addition to addressing these specific challenges, we also develop a working prototype system Aspen, which serves as an end-to-end stream processing system that has been deployed as the foundation for a case study of our SmartCIS application. We validate our solutions both analytically and empirically on top of our prototype system Aspen, over a variety of benchmark workloads such as TPC-H and LinearRoad Benchmarks
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