VAMANA : A High Performance, Scalable and Cost Driven XPath Engine

Many applications are migrating or beginning to make use native XML data. We anticipate that queries will emerge that emphasize the structural semantics of XML query languages like XPath and XQuery. This brings a need for an efficient query engine and database management system tailored for XML data similar to traditional relational engines. While mapping large XML documents into relational database systems while possible, poses difficulty in mapping XML queries to the less powerful relational query language SQL and creates a data model mismatch between relational tables and semi-structured XML data. Hence native solutions to efficiently store and query XML data are being developed recently. However, most of these systems thus far fail to demonstrate scalability with large document sizes, to provide robust support for the XPath query language nor to adequately address costing with respect to query optimization. In this thesis, we propose a novel cost-driven XPath engine to support the scalable evaluation of ad-hoc XPath expressions called VAMANA. VAMANA makes use of an efficient XML repository for storing and indexing large XML documents called the Multi-Axis Storage Structure (MASS) developed at WPI. VAMANA extensively uses indexes for query evaluation by considering index-only plans. To the best of our knowledge, it is the only XML query engine that supports an index plan approach for large XML documents. Our index-oriented query plans allow queries to be evaluated while reading only a fraction of the data, as all tuples for a particular context node are clustered together. The pipelined query framework minimizes the cost of handing intermediate data during query processing. Unlike other native solutions, VAMANA provides support for all 13 XPath axes. Our schema independent cost model provides dynamically calculated statistics that are then used for intelligent cost-based transformations, further improving performance. Our optimization strategy for increasing execution time performance is affirmed through our experimental studies on XMark benchmark data. VAMANA query execution is significantly faster than leading available XML query engines

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

Potentially Polluting Marine Sites GeoDB: An S-100 Geospatial Database as an Effective Contribution to the Protection of the Marine Environment

Potentially Polluting Marine Sites (PPMS) are objects on, or areas of, the seabed that may release pollution in the future. A rationale for, and design of, a geospatial database to inventory and manipu-late PPMS is presented. Built as an S-100 Product Specification, it is specified through human-readable UML diagrams and implemented through machine-readable GML files, and includes auxiliary information such as pollution-control resources and potentially vulnerable sites in order to support analyses of the core data. The design and some aspects of implementation are presented, along with metadata requirements and structure, and a perspective on potential uses of the database

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

A Database Approach to Content-based XML retrieval

This paper describes a rst prototype system for content-based retrieval from XML data. The system's design supports both XPath queries and complex information retrieval queries based on a language modelling approach to information retrieval. Evaluation using the INEX benchmark shows that it is beneficial if the system is biased to retrieve large XML fragments over small fragments

Database independent Migration of Objects into an Object-Relational Database

This paper reports on the CERN-based WISDOM project which is studying the serialisation and deserialisation of data to/from an object database (objectivity) and ORACLE 9i.Comment: 26 pages, 18 figures; CMS CERN Conference Report cr02_01

Innovative Evaluation System – IESM: An Architecture for the Database Management System for Mobile Application

As the mobile applications are constantly facing a rapid development in the recent years especially in the academic environment such as student response system [1-8] used in universities and other educational institutions; there has not been reported an effective and scalable Database Management System to support fast and reliable data storage and retrieval. This paper presents Database Management Architecture for an Innovative Evaluation System based on Mobile Learning Applications. The need for a relatively stable, independent and extensible data model for faster data storage and retrieval is analyzed and investigated. It concludes by emphasizing further investigation for high throughput so as to support multimedia data such as video clips, images and documents

Representing and Utilizing DDI in Relational Databases

This document is primarily intended for implementers of DDI-based metadata stores who are considering different technical options for housing and managing their metadata. The Data Documentation Initiative (DDI) metadata specification is expressed in the form of XML schema. With version 3, the DDI specification has become quite complex, including 21 namespaces and 846 elements. Organizations employing DDI, or considering doing so, may want to 1. store and manage the metadata elements in relational databases, for reasons of integration with existing systems, familiarity with the concepts of relational databases (such as Structured Query Language), systems performance, and/or other reasons; 2. select only the subset of the available DDI metadata elements that is of utility to their work, and have the flexibility of capturing metadata they need that would not fit into the DDI model. This paper discusses advantages and disadvantages of the relational database approach to managing DDI. It also describes methods for modeling DDI in relational databases and for formally defining subsets of DDI to employ in this environment.