What CIOs and CTOs Need to Know About Big Data and Data-Intensive Computing

This paper was completed as part of the final research component in the University of Oregon Applied Information Management Master's Degree Program [see htpp://aim.uoregon.edu].The nature of business computing is changing due to the proliferation of massive data sets referred to as big data, that can be used to produce business analytics (Borkar, Carey, & Li, 2012). This annotated bibliography presents literature published between 2000 and 2012. It provides information to CIOs and CTOs about big data by: (a) identifying business examples, (b) describing the relationship to data-intensive computing, (c) exploring opportunities and limitations, and (d) identifying cost factors

Information visualization: conceptualizing new paths for filtering and navigate in scientific knowledge objects

More than 6,849.32 new research journal articles are published every day! Who has time to read every article or document that’s relevant to their research? Access to the right and relevant information is paramount for scientific discoveries. Filtering relevant information has become a fundamental challenge in the actual scientific deluge panorama. As information glut grows ever worse, understanding and visualizing the science social behavior may become our only hope for handling a growing deluge of scientific information. It is therefore fundamental to analyze and interactively visualize the science social space. This paper theoretically conceptualizes an approach aimed at the filtering and navigation of relevant Scientific Knowledge Objects (SKOs) based on a symbiosis between different sub-disciplines domains. We present two main contributions, a comparison among several projects with some relevant use of information visualization in scholarly scientific navigation; and an architecture which will be in line with the most recent international standards and good practices for Open Data, especially those related to Linked Open Data capable to perform an innovative information visualization of relevant SKOs. These contributions are relevant to scholarly and to practitioner’s communities and to who want to access and navigate in relevant SKOs.This work has been supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Critical analysis of Big Data Challenges and analytical methods

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline

UTILIZING THE POTENTIALS OF BIG DATA IN LIBRARY ENVIRONMENTS IN NIGERIAN FOR RECOMMENDER SERVICES

The big data revolution has gained global attention and initiated creative innovations in every field and libraries as engines of access to information have also been affected by this new trend. Libraries in this part of the world have not utilized the amazing potential of big data in library services. In this time, when various terms such as algorithms age, petabytes age, data age, etc. are been used to describe the activities initiated by machine learning, industries and organizations can achieve much by incorporating inspiring and innovative tools to improve services and performance. In this vein libraries in Nigeria are expected against all odds to make their services more interactive, attractive, innovative, and exciting by utilizing cloud technologies and machine learning techniques to create recommender services. This paper titled “Utilizing the Potentials of Big Data in Nigeria Library Environments by Recommender Services”, focuses on the concept and characteristics of big data and its importance in complementing traditional library services, areas for applying big data systems in libraries, the concept of recommender systems and how it works, adopting recommender systems in libraries for maximum benefits, tools, and techniques for setting up big data recommender systems in libraries, challenges of big data recommender systems in libraries in Nigeria and strategies for overcoming big data challenges in library systems. The paper is based on a contextual analysis of literature from various scholarly works. The paper will also proffer recommendations based on the study

Text mining of biomedical literature: discovering new knowledge

Biomedical literature is increasing day by day. The present scenario shows that the volume of literature regarding “coronavirus” has expanded at a high rate. In this study, text mining technique has been employed to discover something new from the published literature. The main objectives of this study are to show the growth of literature (Jan-Jun, 2020), extract document section, identify latent topics, find the most frequent word, represent the bag of words, and the hierarchical clustering. We have collected 16500 documents from PubMed. This study finds most number of documents (11499) belong to May and June. We explore “betacoronavirus” as the leading document section (3837); “covid” (29890) as the most frequent word in the abstracts; and positive-negative weights of topics. Further, we measure the term frequency (TF) of a document title in the bag of words model. Then we compute a hierarchical clustering of document titles. It reveals that the lowest distance the selected cluster (C133) is 0.30. We also have made a discussion over future prospects and mentioned that this paper can be useful to researchers and library professionals for knowledge management

On the Value of Narratives in a Reflexive Digital Humanities

This paper returns to the relationship of “narrative versus database” (an argument originally made by Lev Manovich in 2001) as one that can be further addressed. A specific issue persists in text analysis research in the digital humanities: the difficulty of representing the figurative meaning of narratives through digital tools. Towards an accommodation, this paper adopts a narratological framework in order to propose alternative models of content management and organization that more closely resemble figurative meaning making in human language. These alternative models therefore better allow for the computational representation of figurative elements that N. Katherine Hayles describes as “the inexplicable, the unspeakable, the ineffable” of narrative literature. This paper argues that the construction of figurative meaning through paradigmatic substitution (as part of an imaginary vocabulary that is drawn from in the process of meaning making) is difficult to account for in the relational database—arguably still the most culturally prominent database model. By focusing on NoSQL (“no” or “not only” Structured Query Language) databases, this paper explores how layers of figurative meaning can be represented together through these flexible and non-relational models. In particular, the ability of non-relational databases to group together multiple values—encouraging their association, comparison, and juxtaposition—can be analyzed as a computational albeit imprecise counterpart to the formation of paradigmatic and figurative meaning. Thus, towards accounting for a word, image, or idea’s layers of meaning as expressed in literature, this paper offers a study of the limitations of digital tools and their critical negotiation with humanities research and reflection