arXiv subjects

Jérôme Darmont

Publications and source records attributed to Jérôme Darmont.

At least 55 records · Page 3Linked to original sources

arXiv2017

Innovative Approaches for efficiently Warehousing Complex Data from the Web

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling and user driven-OLAP analysis.

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arXiv2017

Biomedical Data Warehouses

The aim of this article is to present an overview of the existing biomedical data warehouses and to discuss the issues and future trends in this area. We illustrate this topic by presenting the design of an innovative, complex data warehouse for personal, anticipative medicine.

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arXiv2017

Index and Materialized View Selection in Data Warehouses

The aim of this article is to present an overview of the major families of state-of-the-art index and materialized view selection methods, and to discuss the issues and future trends in data warehouse performance optimization. We particularly focus on data mining-based heuristics we developed to reduce the selection problem complexity and target the most pertinent candidate indexes and materialized views.

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arXiv2017

X-WACoDa: An XML-based approach for Warehousing and Analyzing Complex Data

Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical and practical challenges at the modeling, storage and analysis levels, and a new trend toward XML warehousing has been emerging for a couple of years. Unfortunately, no standard XML data warehouse architecture emerges. In this paper, we propose a unified XML warehouse reference model that synthesizes and enhances related work, and fits into a global XML warehousing and analysis approach we have developed. We also present a software platform that is based on this model, as well as a case study that illustrates its usage.

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arXiv2017

Database Benchmarks

The aim of this article is to present an overview of the major families of state-of-the-art data-base benchmarks, namely: relational benchmarks, object and object-relational benchmarks, XML benchmarks, and decision-support benchmarks, and to discuss the issues, tradeoffs and future trends in database benchmarking. We particularly focus on XML and decision-support benchmarks, which are currently the most innovative tools that are developed in this area.

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arXiv2017

Data Warehouse Benchmarking with DWEB

Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with software benchmarks that help, e.g., test architectural choices, compare different technologies or tune a system. In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist. We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill various data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature as well as its new execution protocol. A Java implementation of DWEB is freely available on-line, which can be interfaced with most existing relational DMBSs. To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.

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arXiv2017

Indices in XML Databases

With XML becoming a standard for business information representation and exchange, stor-ing, indexing, and querying XML documents have rapidly become major issues in database research. In this context, query processing and optimization are primordial, native-XML data-bases not being mature yet. Data structures such as indices, which help enhance performances substantially, are extensively researched, especially since XML data bear numerous specifici-ties with respect to relational data. In this paper, we survey state-of-the-art XML indices and discuss the main issues, tradeoffs and future trends in XML indexing. We also present an in-dex that we specifically designed for the particular architecture of XML data warehouses.

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arXiv2017

Query Performance Optimization in XML Data Warehouses

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this chapter, we present two such techniques. First, we propose a join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. Second, we present a strategy for selecting XML materialized views by clustering the query workload. To validate these proposals, we measure the response time of a set of decision-support XQueries over an XML data warehouse, with and without using our optimization techniques. Our experimental results demonstrate their efficiency, even when queries are complex and data are voluminous.

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arXiv2017

A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related methods.

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arXiv2017

Cost Models for Selecting Materialized Views in Public Clouds

Data warehouse performance is usually achieved through physical data structures such as indexes or materialized views. In this context, cost models can help select a relevant set ofsuch performance optimization structures. Nevertheless, selection becomes more complex in the cloud. The criterion to optimize is indeed at least two-dimensional, with monetary cost balancing overall query response time. This paper introduces new cost models that fit into the pay-as-you-go paradigm of cloud computing. Based on these cost models, an optimization problem is defined to discover, among candidate views, those to be materialized to minimize both the overall cost of using and maintaining the database in a public cloud and the total response time ofa given query workload. We experimentally show that maintaining materialized views is always advantageous, both in terms of performance and cost.

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arXiv2017

A Survey of XML Tree Patterns

With XML becoming an ubiquitous language for data interoperability purposes in various domains, efficiently querying XML data is a critical issue. This has lead to the design of algebraic frameworks based on tree-shaped patterns akin to the tree-structured data model of XML. Tree patterns are graphic representations of queries over data trees. They are actually matched against an input data tree to answer a query. Since the turn of the twenty-first century, an astounding research effort has been focusing on tree pattern models and matching optimization (a primordial issue). This paper is a comprehensive survey of these topics, in which we outline and compare the various features of tree patterns. We also review and discuss the two main families of approaches for optimizing tree pattern matching, namely pattern tree minimization and holistic matching. We finally present actual tree pattern-based developments, to provide a global overview of this significant research topic.

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arXiv2017

Fragmenting very large XML data warehouses via K-means clustering algorithm

XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches fail to achieve. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore , recent research efforts are focusing the attention on fragmentation techniques, which are able to overcome the limitations above. Derived horizontal fragmentation is already used in relational data warehouses, and can definitely be adapted to the XML context. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses, and, with more emphasis, distributed data warehouse architectures. Inspired by this research challenge, in this paper we propose the use of K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses, and, at the same time, completely controlling and determining the number of originated fragments via adequately setting the parameter K. We complete our analytical contribution by means of a comprehensive experimental assessment where we compare the efficiency of our proposed XML data warehouse fragmentation technique against those of classical derived horizontal fragmentation algorithms adapted to XML data warehouses.

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arXiv2017

Benchmarking data warehouses

Data warehouse architectural choices and optimization techniques are critical to decision support query performance. To facilitate these choices, the performance of the designed data warehouse must be assessed, usually with benchmarks. These tools can either help system users comparing the performances of different systems, or help system engineers testing the effect of various design choices. While the Transaction Processing Performance Council's standard benchmarks address the first point, they are not tunable enough to address the second one and fail to model different data warehouse schemas. By contrast, our Data Warehouse Engineering Benchmark (DWEB) allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is implemented as a Java free software that can be interfaced with most existing relational database management systems. The full specifications of DWEB, as well as experiments we performed to illustrate how our benchmark may be used, are provided in this paper.

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arXiv2017

Evaluating the Dynamic Behavior of Database Applications

This paper explores the effect that changing access patterns has on the performance of database management systems. Changes in access patterns play an important role in determining the efficiency of key performance optimization techniques, such as dynamic clustering, prefetching, and buffer replacement. However, all existing benchmarks or evaluation frameworks produce static access patterns in which objects are always accessed in the same order repeatedly. Hence, we have proposed the Dynamic Evaluation Framework (DEF) that simulates access pattern changes using configurable styles of change. DEF has been designed to be open and fully extensible (e.g., new access pattern change models can be added easily). In this paper, we instantiate DEF into the Dynamic object Evaluation Framework (DoEF) which is designed for object databases, i.e., object-oriented or object-relational databases such as multi-media databases or most XML databases.The capabilities of DoEF have been evaluated by simulating the execution of four different dynamic clustering algorithms. The results confirm our analysis that flexible conservative re-clustering is the key in determining a clustering algorithm's ability to adapt to changes in access pattern. These results show the effectiveness of DoEF at determining the adaptability of each dynamic clustering algorithm to changes in access pattern in a simulation environment. In a second set of experiments, we have used DoEF to compare the performance of two real-life object stores : Platypus and SHORE. DoEF has helped to reveal the poor swapping performance of Platypus.

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arXiv2016

A Scalable Document-based Architecture for Text Analysis

Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps %(e.g., stem or lemma extraction, part-of-speech tagging, named entities recognition...), and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. %As a result, no definite solution is currently available. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database. Our experiments demonstrate the feasibility of our approach and the superiority of the document-oriented logical and physical implementation.

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arXiv2016

DESP-C++: A Discrete-Event Simulation Package for C++

DESP-C++ is a C++ discrete-event random simulation engine that has been designed to be fast, very easy to use and expand, and valid. DESP-C++ is based on the resource view. Its complete architecture is presented in detail, as well as a short " user manual ". The validity of DESP-C++ is demonstrated by the simulation of three significant models. In each case, the simulation results obtained with DESP-C++ match those obtained with a validated simulation software: QNAP2. The versatility of DESP-C++ is also illustrated this way, since the modelled systems are very different from each other: a simple production system, the dining philosopher classical deadlock problem, and a complex object-oriented database management system.

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arXiv2016

Benchmarking OODBs with a Generic Tool

We present in this paper a generic object-oriented benchmark (OCB: the Object Clustering Benchmark) that has been designed to evaluate the performances of Object-Oriented Data-bases (OODBs), and more specifically the performances of clustering policies within OODBs. OCB is generic because its sample database may be customized to fit any of the databases in-troduced by the main existing benchmarks, e.g., OO1 (Object Operation 1) or OO7. The first version of OCB was purposely clustering-oriented due to a clustering-oriented workload, but OCB has been thoroughly extended to be able to suit other purposes. Eventually, OCB's code is compact and easily portable. OCB has been validated through two implementations: one within the O2 OODB and another one within the Texas persistent object store. The perfor-mances of a specific clustering policy called DSTC (Dynamic, Statistical, Tunable Clustering) have also been evaluated with OCB.

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arXiv2016

A comparison study of object-oriented database clustering techniques

It is widely acknowledged that a good object clustering is critical to the performance of OODBs. Clustering means storing related objects close together on secondary storage so that when one object is accessed from disk, all its related objects are also brought into memory. Then access to these related objects is a main memory access that is much faster than a disk access. The aim of this paper is to compare the performance of three clustering algorithms: Cactis, CK and ORION. Simulation experiments we performed showed that the Cactis algorithm is better than the ORION algorithm and that the CK algorithm totally out-performs both other algorithms in terms of response time and clustering overhead.

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