60,045 research outputs found

    MORTAL: A Tool of Automatically Designing Relational Storage Schemas for Multi-model Data through Reinforcement Learning

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    Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called MORTAL. Specifically, given multi-model data containing different data models and a set of queries, it could automatically design a relational schema to store these data while having a great query performance. To demonstrate it clearly, we are centered around the following modules: generating initial state based on loaded multi-model data, influencing learning process by setting parameters, controlling generated relational schema through providing semantic constraints, improving the query performance of relational schema by specifying queries, and a highly interactive interface for showing query performance and storage consumption when users adjust the generated relational schema.Peer reviewe

    Designing a resource-efficient data structure for mobile data systems

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    Designing data structures for use in mobile devices requires attention on optimising data volumes with associated benefits for data transmission, storage space and battery use. For semi-structured data, tree summarisation techniques can be used to reduce the volume of structured elements while dictionary compression can efficiently deal with value-based predicates. This project seeks to investigate and evaluate an integration of the two approaches. The key strength of this technique is that both structural and value predicates could be resolved within one graph while further allowing for compression of the resulting data structure. As the current trend is towards the requirement for working with larger semi-structured data sets this work would allow for the utilisation of much larger data sets whilst reducing requirements on bandwidth and minimising the memory necessary both for the storage and querying of the data

    Completing Queries: Rewriting of IncompleteWeb Queries under Schema Constraints

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    Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

    PQL: A Declarative Query Language over Dynamic Biological Schemata

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    We introduce the PQL query language (PQL) used in the GeneSeek genetic data integration project. PQL incorporates many features of query languages for semi-structured data. To this we add the ability to express metadata constraints like intended semantics and database curation approach. These constraints guide the dynamic generation of potential query plans. This allows a single query to remain relevant even in the presence of source and mediated schemas that are continually evolving, as is often the case in data integration

    A unified view of data-intensive flows in business intelligence systems : a survey

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    Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
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