XML content warehousing: Improving sociological studies of mailing lists and web data

In this paper, we present the guidelines for an XML-based approach for the sociological study of Web data such as the analysis of mailing lists or databases available online. The use of an XML warehouse is a flexible solution for storing and processing this kind of data. We propose an implemented solution and show possible applications with our case study of profiles of experts involved in W3C standard-setting activity. We illustrate the sociological use of semi-structured databases by presenting our XML Schema for mailing-list warehousing. An XML Schema allows many adjunctions or crossings of data sources, without modifying existing data sets, while allowing possible structural evolution. We also show that the existence of hidden data implies increased complexity for traditional SQL users. XML content warehousing allows altogether exhaustive warehousing and recursive queries through contents, with far less dependence on the initial storage. We finally present the possibility of exporting the data stored in the warehouse to commonly-used advanced software devoted to sociological analysis

Conceptual Challenges in Contemporary IS Research

This paper, originally prepared as a keynote address for the 1999 Australasian Conference on Information Systems, critically examines some common assumptions underlying much IS research. The assumptions concern the scope and risks of IT projects, the rationales for and uses of IT, and the role of history and time in systems-related outcomes. Making different assumptions about these issues suggests the need for new approaches to IS research

Big Data Coordination Platform: Full Proposal 2017-2022

This proposal for a Big Data and ICT Platform therefore focuses on enhancing CGIAR and partner capacity to deliver big data management, analytics and ICT-focused solutions to CGIAR target geographies and communities. The ultimate goal of the platform is to harness the capabilities of Big Data to accelerate and enhance the impact of international agricultural research. It will support CGIAR’s mission by creating an enabling environment where data are expertly managed and used effectively to strengthen delivery on CGIAR SRF’s System Level Outcome (SLO) targets. Critical gaps were identified during the extensive scoping consultations with CGIAR researchers and partners (provided in Annex 8). The Platform will achieve this through ambitious partnerships with initiatives and organizations outside CGIAR, both upstream and downstream, public and private. It will focus on promoting CGIAR-wide collaboration across CRPs and Centers, in addition to developing new partnership models with big data leaders at the global level. As a result, CGIAR and partner capacity will be enhanced, external partnerships will be leveraged, and an institutional culture of collaborative data management and analytics will be established. Important international public goods such as new global and regional datasets will be developed, alongside new methods that support CGIAR to use the data revolution as an additional means of delivering on SLOs

Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

Ontology-based Knowledge Representation for Protein Data

The advances in information and communication technologies coupled with increased knowledge about genes and proteins have opened new perspectives for study of protein complexes. There is a growing need to integrate the knowledge about various protein complexes for effective disease prevention mechanisms, individualized medicines and treatments and other accepts of healthcare. In this paper we propose a protein ontology that handles the following computational challenges in the area proteomics and systems biology in general: (1) it provides more accurate interpretations and associations as conclusions are based on data and semantics. (2) It makes it possible to study relationships among proteins, protein folding, behaviour of protein under various environments, and most importantly cellular function of protein. This protein ontology is a unified terminology description integrating various protein database schemas and provides a easier way to predict and understand proteins