A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

ERP implementation for an administrative agency as a corporative frontend and an e-commerce smartphone app

This document contains all the descriptions, arguments and demonstrations of the researches, analysis, reasoning, designs and tasks performed to achieve the requirement to technologically evolve an managing agency in a way that, through a solution that requires a reduced investment, makes possible to arrange a business management tool with e-commerce and also a mobile application that allows access and consultation of mentioned tool. The first part of the document describes the scenario in order to contextualize the project and introduces ERP (Enterprise Resources Planning). In the second part, a deep research of ERP market products is carried out, identifying the strengths and weaknesses of each one of the products in order to finish with the choice of the most suitable product for the scenario proposed in the project. A third part of the document describes the installation process of the selected product carried out based on the use of Dockers, as well as the configurations and customizations that they make on the selected ERP. A description of the installation and configuration of additional modules is also made, necessary to achieve the agreed scope of the project. In a fourth part of the thesis, the process of creating an iOS and Android App that connects to the selected ERP database is described. The process begins with the design of the App. Once designed, it is explained the process of study and documentation of technologies to choose the technology stack that allows making an application robust and contemporary without use of licensing. After choosing the technologies to use there are explained the dependencies and needs to install runtime enviornments prior to the start of coding. Later, it describes how the code of the App has been raised and developed. The compilation and verification mechanisms are indicated in continuation. And finally, it is showed the result of the development of the App once distributed. Finally, a chapter for the conclusions analyzes the difficulties encountered during the project and the achievements, analyzing what has been learned during the development of this project

On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

Why and How to Benchmark XML Databases

Benchmarks belong to the very standard repertory of tools deployed in database development. Assessing the capabilities of a system, analyzing actual and potential bottlenecks, and, naturally, comparing the pros and cons of different systems architectures have become indispensable tasks as databases management systems grow in complexity and capacity. In the course of the development of XML databases the need for a benchmark framework has become more and more evident: a great many different ways to store XML data have been suggested in the past, each with its genuine advantages, disadvantages and consequences that propagate through the layers of a complex database system and need to be carefully considered. The different storage schemes render the query characteristics of the data variably different. However, no conclusive methodology for assessing these differences is available to date. In this paper, we outline desiderata for a benchmark for XML databases drawing from our own experience of developing an XML repository, involvement in the definition of the standard query language, and experience with standard benchmarks for relational databases

Why and How to Benchmark XML Databases

Benchmarks belong to the very standard repertory of tools deployed in database development. Assessing the capabilities of a system, analyzing actual and potential bottlenecks, and, naturally, comparing the pros and cons of different systems architectures have become indispensable tasks as databases management systems grow in complexity and capacity. In the course of the development of XML databases the need for a benchmark framework has become more and more evident: a great many different ways to store XML data have been suggested in the past, each with its genuine advantages, disadvantages and consequences that propagate through the layers of a complex database system and need to be carefully considered. The different storage schemes render the query characteristics of the data variably different. However, no conclusive methodology for assessing these differences is available to date. In this paper, we outline desiderata for a benchmark for XML databases drawing from our own experience of developing an XML repository, involvement in the definition of the standard query language, and experience with standard benchmarks for relational databases