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DECLARE and SDS: Early efforts to commercialize deductive database technology

The VLDB Journal 1994年第2期3卷 211-243页

作者： Kleßling, Werner Schmidt, Helmut Strauß, Werner Dünzinger, Gerhard Lehrstuhl für Informatik 2 Universität Augsburg Augsburg D-86135 Universitätsstraße 8 Germany Süddt.Finanzsoftware GmbH Mering D-86415 Reifersbrunner Str. 26 PECOM Unternehmensberatung GmbH München D-81547 Otkerstr. 7b Germany ABLAY Optimierung GmbH Gilching D-82205 Schmidgern 1 Germany

The Smart Data System (SDS) and its declarative query language, Declarative Reasoning, represent the first large-scale effort to commercialize deductive database technology. SDS offers the functionality of deductive reasoning in a distributed, heterogeneous database environment. In this article we discuss several interesting aspects of the query compilation and optimization process. The emphasis is on the query execution plan data structure and its transformations by the optimizing rule compiler. Through detailed case studies we demonstrate that efficient and very compact runtime code can be generated. We also discuss our experiences gained from a large pilot application (the MVV-expert) and report on several issues of practical interest in engineering such a complex system, including the migration from Lisp to C. We argue that heuristic knowledge and control should be made an integral part of deductive databases. © 1994 VLDB.

关键词： Declarative reasoning distributed query processing heuristic control multi-databases productization query optimizer

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Top-k query processing in Probabilistic Databases with Non-Materialized Views

Top-k Query Processing in Probabilistic Databases with Non-M...

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29th IEEE International Conference on Data Engineering (ICDE)

作者： Dylla, Maximilian Miliaraki, Iris Theobald, Martin Max Planck Inst Informat Campus E1-4 D-66123 Saarbrucken Germany Univ Antwerp B-2020 Antwerp Belgium

ISBN: (纸本)9781467349093;9781467349086

We investigate a novel approach of computing confidence bounds for top-k ranking queries in probabilistic databases with non-materialized views. Unlike related approaches, we present an exact pruning algorithm for finding the top-ranked query answers according to their marginal probabilities without the need to first materialize all answer candidates via the views. Specifically, we consider conjunctive queries over multiple levels of select-project-join views, the latter of which are cast into Datalog rules which we ground in a top-down fashion directly at query processing time. To our knowledge, this work is the first to address integrated data and confidence computations for intensional query evaluations in the context of probabilistic databases by considering confidence bounds over first-order lineage formulas. We extend our query processing techniques by a tool-suite of scheduling strategies based on selectivity estimation and the expected impact on confidence bounds. Further extensions to our query processing strategies include improved top-k bounds in the case when sorted relations are available as input, as well as the consideration of recursive rules. Experiments with large datasets demonstrate significant runtime improvements of our approach compared to both exact and sampling-based top-k methods over probabilistic data.

关键词： query analysis confidence Bound Database View Exact distributed query processing relational query processing

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A High-Performance distributed Relational Database System for Scalable OLAP processing 33

A High-Performance Distributed Relational Database System fo...

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33rd IEEE International Parallel and distributed processing Symposium (IPDPS)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Chicago IL 60616 USA

ISBN: (纸本)9781728112466

The scalability of systems such as Hive and Spark SQL that are built on top of big data platforms have enabled query processing over very large data sets. However, the per-node performance of these systems is typically low compared to traditional relational databases. Conversely, Massively Parallel processing (MPP) databases do not scale as well as these systems. We present HRDBMS, a fully implemented distributed shared-nothing relational database developed with the goal of improving the scalability of OLAP queries. HRDBMS achieves high scalability through a principled combination of techniques from relational and big data systems with novel communication and work-distribution techniques. While we also support serializable transactions, the system has not been optimized for this use case. HRDBMS runs on a custom distributed and asynchronous execution engine that was built from the ground up to support highly parallelized operator implementations. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS's scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases like Greenplum.

关键词： SQL big data distributed query processing

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HRDBMS: A NewSQL Database for Analytics

HRDBMS: A NewSQL Database for Analytics

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781467365987

HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regarding query optimization, while also taking advantage of the scalability of Big Data platforms. The system uses an execution framework that is tailored for relational processing, thus addressing some of the performance challenges of running SQL on top of platforms such as MapReduce and Spark. These include excessive materialization of intermediate results, lack of a global cost based optimization, unnecessary sorting, lack of index support, no statistics, no support for DML and ACID, and excessive communication caused by the rigid communication patterns enforced by these platforms.

关键词： SQL Big Data analytics distributed query processing relational databases

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Probabilistic Threshold Join over distributed Uncertain Data

Probabilistic Threshold Join over Distributed Uncertain Data

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12th International Conference on Web-Age Information Management

作者： Deng, Lei Wang, Fei Huang, Benxiong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9783642235344

Large amount of uncertain data is collected by many emerging applications which contain multiple sources in a distributed manner. Previous efforts on querying uncertain data in distributed environment have only focus on ranking and skyline, join queries have not been addressed in earlier work despite their importance in databases. In this paper, we address distributed probabilistic threshold join query, which retrieves results satisfying the join condition with combining probabilities that meet the threshold requirement from distributed sites. We propose a new kind of bloom filters called Probability Bloom Filters (PBF) to represent set with probabilistic attribute and design a PBF based Bloomjoin algorithm for executing distributed probabilistic threshold join query with communication efficiency. Furthermore, we provide theoretical analysis of the network cost of our algorithm and demonstrate it by simulation. The experiment results show that our algorithm can save network cost efficiently by comparing to original Bloomjoin algorithm in most scenarios.

关键词： distributed query processing joins uncertain data Bloom filters

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Elastic Pipelining in an In-Memory Database Cluster 16

Elastic Pipelining in an In-Memory Database Cluster

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ACM SIGMOD International Conference on Management of Data

作者： Wang, Li Zhou, Minqi Zhang, Zhenjie Yang, Yin Zhou, Aoying Bitton, Dina East China Normal Univ Inst Data Sci & Engn Shanghai Peoples R China Illinois Singapore Pte Ltd Adv Digital Sci Ctr Singapore Singapore Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Bitton Consulting Los Angeles CA USA

ISBN: (纸本)9781450335317

An in-memory database cluster consists of multiple interconnected nodes with a large capacity of RAM and modern multi-core CPUs. As a conventional query processing strategy, pipelining remains a promising solution for in-memory parallel database systems, as it avoids expensive intermediate result materialization and parallelizes the data processing among nodes. However, to fully unleash the power of pipelining in a cluster with multi-core nodes, it is crucial for the query optimizer to generate good query plans with appropriate intra-node parallelism, in order to maximize CPU and network bandwidth utilization. A suboptimal plan, on the contrary, causes load imbalance in the pipelines and consequently degrades the query performance. Parallelism assignment optimization at compile time is nearly impossible, as the workload in each node is affected by numerous factors and is highly dynamic during query evaluation. To tackle this problem, we propose elastic pipelining, which makes it possible to optimize intra-node parallelism assignments in the pipelines based on the actual workload at runtime. It is achieved with the adoption of new elastic iterator model and a fully optimized dynamic scheduler. The elastic iterator model generally upgrades traditional iterator model with new dynamic multi-core execution adjustment capability. And the dynamic scheduler efficiently provisions CPU cores to query execution segments in the pipelines based on the light-weight measurements on the operators. Extensive experiments on real and synthetic (TPC-H) data show that our proposal achieves almost full CPU utilization on typical decision-making analytical queries, outperforming state-of-the-art open-source systems by a huge margin.

关键词： query processing in-memory database multi-core numa architecture distributed query processing

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Mobile Agent-based Dynamic Resource Allocation Method for query Optimization in Data Grid Systems

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7th KES Conference on Agent and Multi-Agent Systems - Technologies and Applications (KES-AMSTA)

作者： Epimakhov, Igor Hameurlain, Abdelkader Morvan, Franck Yin, Shaoyi Univ Toulouse 3 Inst Rech Informat Toulouse IRIT F-31062 Toulouse France

ISBN: (纸本)9781614992547;9781614992530

Resource allocation is one of the principal stages of query processing in relational data grid systems. Specific characteristics of the data grid environment, such as dynamicity, heterogeneity and large scale, impose serious restrictions to the resource allocation process. Static resource allocation before the query execution may be far from optimal due to the dynamic changes of the system. One possible optimization is to adjust dynamically the allocation of resources during the query execution. Some methods of dynamic resource allocation have been proposed, however, most of them use centralized control mechanisms. In this study we argue that the decentralized approach meets better the requirements of the data grid systems. In this study we propose a decentralized method of dynamic resource allocation that is based on the mobile agent paradigm. We consider the participating nodes as autonomous and independent elements of the system, each of which can detect if it is overloaded and make the decision to react. Then we consider each relational operation as a mobile agent running on the allocated node, meaning that, it keeps track of its own status and can migrate to another node at any time. A two-level cooperation mechanism between such autonomous nodes and autonomous operations is described in detail. Performance evaluation proves the efficiency of the proposed method.

关键词： Data grid systems distributed query processing optimization resource allocation load balancing scheduling

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Partout: A distributed Engine for Efficient RDF processing 23

Partout: A Distributed Engine for Efficient RDF Processing

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23rd International Conference on World Wide Web (WWW)

作者： Galarraga, Luis Hose, Katja Schenkel, Ralf Telecom ParisTech Paris France Aalborg Univ Aalborg Denmark Univ Passau Passau Germany

ISBN: (纸本)9781450327459

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications relying on efficient query processing. Confronted with such a trend, existing centralized state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce PARTOUT, a distributed engine for fast RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log and allocating the fragments to hosts in a cluster of machines. Furthermore, PARTOUT's query optimizer produces efficient query execution plans for ad-hoc SPARQL queries.

关键词： Semantic Web distributed Systems RDF Data Partitioning distributed query processing

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Generating distributed query Plans Using Modified Cuckoo Search Algorithm 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Kumar, T. V. Vijay Yadav, Monika Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (数字)9789811033223

ISBN: (纸本)9789811033223;9789811033216

In distributed databases, data is replicated and fragmented across multiple disparate sites spread across a computer network. Consequently, there can exist large numbers of possible query plans for a distributed query. This number increases with increase in the number of sites containing the replicated data. For large numbers of sites, computing an efficient query processing plan becomes a computationally expensive task. This necessitates the devising of a distributed query processing strategy capable of generating good quality query plans, from amongst all possible query plans, which minimize the total cost of processing a distributed query. This distributed query plan generation (DQPG) problem, being a combinatorial optimization problem, has been addressed in this paper using the modified cuckoo search algorithm. Accordingly, a modified CSA (mCSA) based DQPG algorithm (DQPGmCSA), which aims to generate good quality Top-K query plans for a given distributed query, has been proposed herein. Experimental based comparison of DQPGmCSA with the existing GA based DQPG algorithm (DQPGGA) shows that the former is able to generate comparatively better quality Top-K query plans, which, in turn, would result in a reduction in the query response time and thereby enabling efficient decision making.

关键词： distributed query processing distributed query Plan Generation (DQPG) Swarm intelligence Cuckoo Search Algorithm (CSA)

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Insertion and querying Mechanism for a distributed XML Database System 12

Insertion and Querying Mechanism for a Distributed XML Datab...

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5th ACM COMPUTE Conference

作者： Kulkarni, Abhinandan M. Thirunavukkarasu, Janani Pillai, Parvathy S. Sulegai, Sneha Somanath Rao, Shrisha Int Inst Informat Technol Bangalore Bangalore Karnataka India

ISBN: (纸本)9781450314404

XML fi les are ideal for describing semi structured data. The advent of native XML databases has enhanced storage of a large number of XML fi les preserving the hierarchical format. The challenge is to serve and access XML data from geographically distributed native XML databases. In this paper, we discuss the modus operandi for insertion and querying of data in a location transparent manner in decentralised networks. We present the design for a cluster based architecture where query processingF is achieved using distributed hash tables. A bulk insertion method has been employed for insertion of XML data. The proposed design was implemented on eXist-db, a native XML database engine. Experiments were conducted and the effectiveness of the proposed design was evaluated.

关键词： distributed XML Databases Native XML Databases Bulk Insertion distributed query processing

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