Implementation of multidimensional databases in column-oriented NoSQL systems

International audienceNoSQL (Not Only SQL) systems are becoming popular due to known advantages such as horizontal scalability and elasticity. In this paper, we study the implementation of multidimensional data warehouses with columnoriented NoSQL systems. We define mapping rules that transform the conceptual multidimensional data model to logical column-oriented models. We consider three different logical models and we use them to instantiate data warehouses. We focus on data loading, model-to-model conversion and OLAP cuboid computation

Implementing Multidimensional Data Warehouses into NoSQL

International audienceNot only SQL (NoSQL) databases are becoming increasingly popular and have some interesting strengths such as scalability and flexibility. In this paper, we investigate on the use of NoSQL systems for implementing OLAP (On-Line Analytical Processing) systems. More precisely, we are interested in instantiating OLAP systems (from the conceptual level to the logical level) and instantiating an aggregation lattice (optimization). We define a set of rules to map star schemas into two NoSQL models: columnoriented and document-oriented. The experimental part is carried out using the reference benchmark TPC. Our experiments show that our rules can effectively instantiate such systems (star schema and lattice). We also analyze differences between the two NoSQL systems considered. In our experiments, HBase (columnoriented) happens to be faster than MongoDB (document-oriented) in terms of loading time

DBMS TRANSACTION TRANSLATION

Data translation and transaction translation are two major problems that have to be solved in order to achieve the coexistence of heterogeneous distributed databases. In this paper we discuss the problem of transaction translation. The nature of the problem is explored by developing direct translations of transactions between the relational and hierarchical and network models. Methods for mapping a hierarchical or network schema to an equivalent relational schema are presented. The relational operators projection, selection, join, insertion. deletion and update are translated to equivalent hierarchical and network operations.Information Systems Working Papers Serie

A relational post-processing approach for forms recognition

Optical Character Recognition (OCR) is used to convert paper documents into electronic form. Unfortunately the technology is not perfect and the output can be erroneous. Conversion then is generally augmented by manual error detection and correction procedures which can be very costly; One approach to minimizing cost is to apply an OCR post processing system that will reduce the amount of manual correction required. The post processor takes advantage of knowledge associated with a particular project; In this thesis, we look into the feasibility of using integrity constraints to detect and correct errors in forms recognition. The general idea is to construct a database of form values that can be used to direct recognition and consequently, make automatic correction

A SQL front-end semantic data model

SQLSDM is a front end semantic data model to a SQL relational database management system (RDBMS). SQLSDM provides a more semantically complete RDBMS through the implementation of a Domain and Relational Integrity scheme. SQLSDM provides integrity definition functions and a sub-system to interpret SQL commands . Integrity system tables are created through the use of SQLSDM \u27 s domain definition command and SQL \u27 s CREATE TABLE command. As SQL database update commands are interpreted, SQLSDM uses these integrity tables to enforce domain and referential integrity. SQLSDM operates virtually transparent to the user and provides for greater database consistency and semantic control. Furthermore, SQLSDM is designed and engineered to be a portable front-end that may be implemented on any SQL relational database management system

RDB to 의미 데이터 변환기법에 기반한 의미 데이터 생성 및 활용 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김형주.RDB to RDF transformation is a semantic information extraction method that supports the Semantic Web. The direct mapping, one of the RDB to RDF transformation methods, is a representative mapping method recommended by the W3C. The direct mapping processes an automatic mapping from relational data to RDF data. Semantics preservation is an important property of the direct mapping to transform relational data to semantic data without information loss. However, existing direct mapping methods have problems that violate semantics preservation in specific cases. To comply with the semantics preservation, a hierarchical direct mapping method is provided. Rules of the hierarchical direct mapping are defined based on lemmas that represent features of semantic data transformation. A hierarchical semantic vocabulary is also defined to generate sound and precise semantic data. Next, this thesis also focused on developing an effective direct mapping to generate lightweight and intuitive semantic output data. Thus, the optimized hierarchical direct mapping is provided based on a relational meta-schema vocabulary. Rules of multi-column keys are defined to reduce repetitive constraint data generation problems. Rules for multiple keys are also defined because relational tables may contain multiple foreign keys or unique constraints that affect the output data size. The relational meta-schema vocabulary describes concepts of relational data and relationships among the concepts. The optimized hierarchical mapping method uses initially defined relational concepts from the vocabulary, and generates compact and intuitive semantic output data. Finally, a semantic metadata based information retrieval method is provided as semantic data utilization. Existing ranking methods do not have direct methods of evaluating the meaning of links. In this thesis, a semantic metadata based ranking approach is proposed to directly analyze the meaning of links by using a semantic Web data structure. The semantic Web data structure is built upon semantic metadata extracted from the Web data by using the RDB to RDF transformation method described above. The provided method evaluates the weight of the links for stratifying rank values based on their importance in the semantic Web data structure. The experimental results showed that the proposed mapping method performs semantics preserving RDB to RDF transformation and outputs smaller size semantic data with better quality, and the weighted semantic metadata based ranking approach outperforms existing methods.1 Introduction 1 1.1 Research Motivation 1 1.2 Research Contributions 4 1.3 Outline 9 2 Preliminaries 11 2.1 RDF 11 2.2 RDFS 14 2.3 RDFa 15 2.4 OWL 16 2.5 RDB to RDF Transformation 18 2.6 Terminologies 33 3 Semantics Preserving RDB to RDF Transformation 34 3.1 Motivation 35 3.2 Base Definitions of Predicates 37 3.3 Semantics Preservation 38 3.4 Problem Description 40 3.5 Mapping Rules 43 3.6 Evaluation 63 4 Repetitive Data Reduction Methods for RDB to RDF Transformation 69 4.1 Motivation 69 4.2 Base Definitions of Predicates 74 4.3 Mapping Multi-column Key 75 4.4 Mapping Multiple Keys 89 4.5 Relational Meta-schema Vocabulary 97 4.6 Evaluation 109 5 Utilization of RDB to RDF Transformation for Information Retrieval 119 5.1 Motivation 119 5.2 Previous Work 122 5.3 Semantic Metadata Annotation Using RDB to RDF transformation 125 5.4 Information Retrieval Based on Weighted Semantic Resource Rank 127 5.5 Evaluation 139 6 Conclusions and Future Work 144 6.1 Conclusions 144 6.2 Future Work 147 Appendices 149 A Proofs 149 A.1 Proof of Lemma 1 149 A.2 Proof of Lemma 2 150 A.3 Proof of Lemma 3 151 A.4 Proof of Lemma 4 153 A.5 Proof of Theorem 1 154 B Semi-automatic Semantic Data Publication 155 C Specifications 158 C.1 Hierarchical Semantic Vocabulary 158 C.2 Relational Meta-Schema Vocabulary 160 Bibliography 165 초 록 177Docto