Toward a tenant-aware query rewriting engine for Universal Table schema-mapping

DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

Computer Engineering Science and System Journal ◽

10.24114/cess.v3i2.8895 ◽

2018 ◽

Vol 3 (2) ◽

pp. 162

Author(s):

Slamet Sudaryanto Nurhendratno ◽

Sudaryanto Sudaryanto

Keyword(s):

Data Integration ◽

Data Center ◽

Data Exchange ◽

Heterogeneous Data ◽

Data Sources ◽

Query Rewriting ◽

Schema Mapping ◽

Data Interoperability ◽

Integration Model ◽

Data Source

Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

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Implementation of RDB(Relational DataBases) Tables Using RDF(Resource Description Framework) Schema Mapping Modeling

The Journal of Next-generation Convergence Technology Association ◽

10.33097/jncta.2020.04.04.381 ◽

2020 ◽

Vol 4 (4) ◽

pp. 381-387

Author(s):

Ju-Ri Kim ◽

Keyword(s):

Resource Description Framework ◽

Relational Databases ◽

Schema Mapping ◽

Description Framework ◽

Resource Description

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Query rewriting for SWIFT (First) answers

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/69.877503 ◽

2000 ◽

Vol 12 (5) ◽

pp. 694-714 ◽

Cited By ~ 1

Author(s):

Kian-Lee Tan ◽

Cheng Hian Goh ◽

Beng Chin Ooi

Keyword(s):

Query Rewriting

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Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽

10.3390/a14050149 ◽

2021 ◽

Vol 14 (5) ◽

pp. 149

Author(s):

Petros Zervoudakis ◽

Haridimos Kondylakis ◽

Nicolas Spyratos ◽

Dimitris Plexousakis

Keyword(s):

Query Optimization ◽

Query Language ◽

Computational Cost ◽

Continuous Queries ◽

Continuous Query ◽

Query Rewriting ◽

Query Evaluation ◽

Clear Separation ◽

Complete Dataset ◽

High Level

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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