Engage - Using Data About Research Clusters to Enhance Collaboration

This project explored different classifications of research and ideas for implementing these in University systems to facilitate publicity of research

A framework for an Integrated Mining of Heterogeneous data in decision support systems

The volume of information available on the Internet and corporate intranets continues to increase along with the corresponding increase in the data (structured and unstructured) stored by many organizations. Over the past years, data mining techniques have been used to explore large volume of data (structured) in order to discover knowledge, often in form of a decision support system. For effective decision making, there is need to discover knowledge from both structured and unstructured data for completeness and comprehensiveness. The aim of this paper is to present a framework to discover this kind of knowledge and to present a report on the work-in-progress on an on going research work. The proposed framework is composed of three basic phases: extraction and integration, data mining and finally the relevance of such a system to the business decision support system. In the first phase, both the structured and unstructured data are combined to form an XML database (combined data warehouse (CDW)). Efficiency is enhanced by clustering of unstructured data (documents) using SOM (Self Organized Maps) clustering algorithm, extracting keyphrases based on training and TF/IDF (Term Frequency/Inverse Document Frequency) by using the KEA (Keyphrases Extraction Algorithm) toolkit. In the second phase, association rule mining technique is applied to discover knowledge from the combined data warehouse. The final phase reflects the changes that such a system will bring about to the marketing decision support system. The paper also describes a developed system which evaluates the association rules mined from structured data that forms the first phase of the research work. The proposed system is expected to improve the quality of decisions, and this will be evaluated by using standard metrics for evaluating the interestingness of association rule which is based on statistical independence and correlation analysis

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A framework for utility data integration in the UK

In this paper we investigate various factors which prevent utility knowledge from being fully exploited and suggest that integration techniques can be applied to improve the quality of utility records. The paper suggests a framework which supports knowledge and data integration. The framework supports utility integration at two levels: the schema and data level. Schema level integration ensures that a single, integrated geospatial data set is available for utility enquiries. Data level integration improves utility data quality by reducing inconsistency, duplication and conflicts. Moreover, the framework is designed to preserve autonomy and distribution of utility data. The ultimate aim of the research is to produce an integrated representation of underground utility infrastructure in order to gain more accurate knowledge of the buried services. It is hoped that this approach will enable us to understand various problems associated with utility data, and to suggest some potential techniques for resolving them

Business Intelligence And Geographic Information System Lifecycle Architecture Using Cloud Computing For Smart Community

Business Intelligence (BI) is a technique and IT tool that supports business decision making. BI is considered a unique source of competitive advantage in the market place and it can combine data, multimedia, and transactions all in one application to address people\u27s needs. Using BI applications can increase both operational efficiency and customer satisfaction. In addition, BI improves planning that provides the foundation for top successful performances in the future. Geographic Information Systems (GIS) deliver productivity tools that are highly beneficial for businesses. These benefits include: visualization, business capacity, analysis, and interpreting data to understand relationships, patterns, and trends. Another benefit is a new feature can report drivers who speed, accelerate hard, and make sudden stops. Nowadays, GIS is responsible for developing standard, strategy, and policy that emphasize coordination and cooperation among organizations and businesses in order to maximize cost effectiveness. Smart card technology is the name that describes plastic cards with an embedded computer chip. Basically, smart cards are usually the most cost-effective solution because it increases the level of processing power, memory, and flexibility. Therefore, implementing a smart card in a driver\u27s license can help to build control in a community. However, the general objective of the study is to investigate the feasibility of integrating Cloud Computing, BI, and GIS to build smart community. The research focuses on enhancing GIS tools and build control in community. The proposed system is a new approach in Information Technology that can lower business cost and build policy in organization. The research is nonexperimental study used qualitative method to answer certain research questions posed for the study. The result obtained from analysis exists GIS showed that enhance GIS can build control in the community. Furthermore, there is a feasibility integrating BI, Cloud and GIS to build smart community. On the bases of these finding, IT synergy will influence the business workflow process and performance of organization. Organizations can achieve many benefits from the integrating Cloud Computing approach, BI and GIS in decision making