NITELIGHT: A Graphical Tool for Semantic Query Construction

Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries

Using software visualization technology to help genetic algorithm designers

This work is part of a three year PhD project to examine how Software Visualization(SV) can be applied to support the design and construction of Genetic Algorithms (GAs). A user survey carried out at the start of this project identified a set of key system features required by GA users. A visualization system embodying these features was then designed and a prototype built. This paper describes what genetic algorithms are and how they can be applied. It then reviews some of the survey results and their impact on the design of the visualization system. The paper concludes with an exploration of how the resulting prototype may be evaluated

Exploring user and system requirements of linked data visualization through a visual dashboard approach

One of the open problems in SemanticWeb research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the user’s point of view, their tasks and interaction, as well as the system’s capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualising linked data. In light of the findings, guidelines for the development of linked data visualization (and manipulation) are provided

Taking Afrobarometer Data Everywhere

According to statistics gathered by research group Afrobarometer, many countries in Africa lack infrastructure and basic necessities. In fact, Afrobarometer knows the specific rates of need and availability sampled across thirty-six countries but more prosperous African countries do not know these numbers. These more developed countries are in a position to help their less fortunate neighbors if only made aware of the social and economic climate in the respective areas. Our partnership with Afrobarometer will allow us to advertise these statistics through the use of a mobile application. The data will be displayed in a way that is easy for the average reader to digest and understand. By exposing a larger African audience to the results from these public opinion surveys, Afrobarometer hopes to inspire these people to take action and make donations to the appropriate social benefit groups. The countries represented by the surveys can then receive help in the areas expressing need

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