Native Language OLAP Query Execution

Online Analytical Processing (OLAP) applications are widely used in the components of contemporary Decision Support systems. However, existing OLAP query languages are neither efficient nor intuitive for developers. In particular, Microsoft’s Multidimensional Expressions language (MDX), the de-facto standard for OLAP, is essentially a string-based extension to SQL that hinders code refactoring, limits compile-time checking, and provides no object-oriented functionality whatsoever. In this thesis, we present Native language OLAP query eXecution, or NOX, a framework that provides responsive and intuitive query facilities. To this end, we exploit the underlying OLAP conceptual data model and provide a clean integration between the server and the client language. NOX queries are object-oriented and support inheritance, refactoring and compile-time checking. Underlying this functionality is a domain specific algebra and language grammar that are used to transparently convert client side queries written in the native development language into algebraic operations understood by the server. In our prototype of NOX, JAVA is used as the native language. We provide client side libraries that define an API for programmers to use for writing OLAP queries. We investigate the design of NOX through a series of real world query examples. Specifically, we explore the following: fundamental SELECTION and PROJECTION, set operations, hierarchies, parametrization and query inheritance. We compare NOX queries to MDX and show the intuitiveness and robustness of NOX. We also investigate NOX expressiveness with respect to MDX from an algebraic point of view by demonstrating the correspondence of the two approaches in terms of SELECTION and PROJECTION operations. We believe the practical benefit of NOX-style query processing is significant. In short, it largely reduces OLAP database access to the manipulation of client side, in-memory data object

HaoLap: a Hadoop based OLAP system for big data

International audienceIn recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multidimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations

A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities

To face the tough competition, changing markets and technologies in automotive industry, automakers have to be highly innovative. In the previous decades, innovations were electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal network. Furthermore, the growing expectations and preferences of customers oblige these manufacturers to adapt their business models and to also propose mobility-based services. One other hand, there is also an increasing pressure from regulators to significantly reduce the environmental footprint in transportation and mobility, down to zero in the foreseeable future. This dissertation investigates an architecture for communication and data exchange within a complex and heterogeneous ecosystem. This communication takes place between various third-party entities on one side, and between these entities and the infrastructure on the other. The proposed solution reduces considerably the complexity of vehicle communication and within the parties involved in the ODX life cycle. In such an heterogeneous environment, a particular attention is paid to the protection of confidential and private data. Confidential data here refers to the OEM’s know-how which is enclosed in vehicle projects. The data delivered by a car during a vehicle communication session might contain private data from customers. Our solution ensures that every entity of this ecosystem has access only to data it has the right to. We designed our solution to be non-technological-coupling so that it can be implemented in any platform to benefit from the best environment suited for each task. We also proposed a data model for vehicle projects, which improves query time during a vehicle diagnostic session. The scalability and the backwards compatibility were also taken into account during the design phase of our solution. We proposed the necessary algorithms and the workflow to perform an efficient vehicle diagnostic with considerably lower latency and substantially better complexity time and space than current solutions. To prove the practicality of our design, we presented a prototypical implementation of our design. Then, we analyzed the results of a series of tests we performed on several vehicle models and projects. We also evaluated the prototype against quality attributes in software engineering

Advanced distributed data integration infrastructure and research data management portal

The amount of data available due to the rapid spread of advanced information technology is exploding. At the same time, continued research on data integration systems aims to provide users with uniform data access and efficient data sharing. The ability to share data is particularly important for interdisciplinary research, where a comprehensive picture of the subject requires large amounts of data from disparate data sources from a variety of disciplines. While there are numerous data sets available from various groups worldwide, the existing data sources are principally oriented toward regional comparative efforts rather than global applications. They vary widely both in content and format. Such data sources cannot be easily integrated, and maintained by small groups of developers. I propose an advanced infrastructure for large-scale data integration based on crowdsourcing. In particular, I propose a novel architecture and algorithms to efficiently store dynamically incoming heterogeneous datasets enabling both data integration and data autonomy. My proposed infrastructure combines machine learning algorithms and human expertise to perform efficient schema alignment and maintain relationships between the datasets. It provides efficient data exploration functionality without requiring users to write complex queries, as well as performs approximate information fusion when exact match does not exist. Finally, I introduce Col*Fusion system that implements the proposed advance data integration infrastructure

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

L'AIS : une donnée pour l'analyse des activités en mer

4 pages, session "Mer et littoral"International audienceCette contribution présente des éléments méthodologiques pour la description des activités humaines en mer dans une perspective d'aide à la gestion. Différentes procédures, combinant l'exploitation de bases de données spatio-temporelles issue de données AIS archivées à des analyses spatiales au sein d'un SIG, sont testées afin de caractériser le transport maritime en Mer d'Iroise (Bretagne, France) sur les plans spatiaux, temporels et quantitatifs au cours d'une année

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

International Navigation Market

Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors such as diversification of trade, number of participants in this process and the increasingly complex international trade

International Navigation Market

Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors such as diversification of trade, number of participants in this process and the increasingly complex international trade