The International Journal of Information Technology & Decision Making (IJITDM) provides a global forum for exchanging research findings and case studies which bridge the latest information technology and various decision-making techniques. It promotes how information technology improves decision techniques as well as how the development of decision-making tools affects the information technology era. The journal is peer-reviewed and publishes both high-quality academic (theoretical or empirical) and practical papers in the broad ranges of information technology related topics.

The International Journal of Information Technology & Decision Making has an impact factor of 3.139 and the journal enjoys the following rankings:

• 7th out of 108 in COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
• 5th out of 126 in COMPUTER SCIENCE, INFORMATION SYSTEMS
• 7th out of 97 in COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
• 3rd out of 74 in OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Here is a summary of this article:

Degree centrality is considered to be one of the most basic measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasizes the number of connections that an actor has with others. However, it does not accommodate the value of the duration of relations with other actors in a network; and therefore, this traditional degree centrality approach regards only the presence or absence of links. Here, we introduce a time-variant approach to the degree centrality measure – time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network. We illustrate the difference between the traditional and time scale degree centrality measure by applying these two approaches to explore the impact of degree attributes of a patient-physician network evolving during patient hospitalization periods on the hospital length of stay (LOS) both at a macro- and a micro-level. At a macro-level, both the traditional and time-scale approaches to degree centrality can explain the relationship between the degree attribute of the patient-physician network and LOS. However, at a micro-level or small cluster level, TSDC provides better explanation while the traditional degree centrality approach is found to be inadequate in explaining its relationship with LOS. This measure (i.e. TSDC) can explore time-variant relations that evolve among actors of a given social network.

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In his talk, Dr. Agarwal discussed his research projects in social computing, especially the promising role of social media in socio-political and economic transformation of the Arab world in specific and society in general. Taking examples from the Arab Spring, the Saudi Arabian women’s movements to protest against inequitable rights, and crisis management during natural disasters, Dr. Agarwal illustrated the important role of social media in mobilization and manifestation of collective actions and how such amorphous and ‘unorganized’ organizations emerge and work effectively in cross-cultural settings. Implications of this research could be envisioned in (but not limited to) promoting citizen engagement towards better governance and policy making, developing smart and connected health applications for the well-being of the society, advancing collective learning paradigms to facilitate massive and open education, and exploring the burgeoning “crowdfunding” model in the micro-financing sector. More details on these federally funded projects could be obtained from http://ualr.edu/nxagarwal/.

Dr. Agarwal further emphasized the need for reaching out to collaborations from different disciplines such as, sociology, psychology, cognitive and behavioral sciences, mathematics, statistics, among others. This is of utmost importance for the growth of interdisciplinary research areas, such as social computing, and for the synergistic advancement of the multiple disciplines involved. This is especially true in the age of big data. Just as the data is coming from diverse sources so should the perspectives of analysis be to avoid falling into the “data-fishing” trap. Researchers with same disciplinary background may lack insights to guide the analysis or even shape the hypothesis.

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Called “Summer at Tec^Edge” (SATE) by its director, Dr. Rob Williams, it is an open -innovation project that brings students, faculty, and AFRL personnel together for intense collaboration in a non-classified environment. The UALR students and faculty worked on the “4-D Information Quality” team led by Dr. Rhonda Vickery from Mississippi State University. The team focused on ways to visualize surveillance information in 3 dimensions plus the time dimension (hence 4-D) and to develop information quality tools for surveillance data sets. UALR faculty mentors who worked with the students in Dayton were Dr. Edi Tudoreanu and Dr. John Talburt from the UALR Information Science Department and Dr. Mariofanna Milanova from the UALR Computer Science Department. The 4DIQ Team presented its results in a poster session at the SATE Open House on August 6, 2009. Some of their accomplishments include developing a combination objective-subjective framework for video quality assessment, using geo-located Twitter message traffic as a sensor layer, migration of the NASA WorldWind geographic rendering system into the UALR CAVE environment, and conversion of several data source interfaces to the AFRL Layered Sensor Table (LST) system from C# to Java. The UALR team also contributed to the CityBeat, Calamityville, and Gotcha! team projects.

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Dr. Wang, recognized as a world-class expert on data quality matters, will provide advice to Army Governance and Executive Boards consisting of the Senior Military Leadership across the Enterprise including Army Secretariat and Staffs. Additionally, Dr. Wang will recommend strategies and solutions that will significantly streamline management burdens and reduce overhead and operating costs of Army Data Management Programs.  He will also be responsible for developing and implementing data quality strategies and criteria to manage data elements across the Army Enterprise, ensuring that data is trusted.

Dr. Wang was instrumental in creating the Master of Science in Information Quality (MSIQ) degree at UALR in 2006.  The MSIQ program established at UALR was the world’s first graduate degree in information quality and is the still the only program of its kind in the U.S. (http://ualr.edu/eit/iq).  In 2008, UALR added an Information Quality Emphasis to its Applied Science PhD program.  The total enrollment for both programs in for the spring 2009 semester stands at 62 students.  Dr. Wang also serves as the Co-Director of the UALR Laboratory for Advanced Research in Entity Resolution and Information Quality (http://ualr.edu/eriq/).

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Nitin Agarwal expects to be awarded the Ph.D. before beginning his duties at UALR. He already has published four journal papers, nine conference papers, four book chapters, and submitted four other papers. These have appeared in such prestigious forums as ACM Transactions on Knowledge Discovery from Data, and the International Conference on Web Search and Data Mining. He was recently recognized as an Outstanding PhD Student finalist by the School of Computing and Informatics at Arizona State University.

UALR’s Department of Information Science expects its students to benefit directly from Agarwal’s teaching and research expertise in the form of new courses and projects at both the undergraduate and graduate levels. He will complement a department with curriculum, teaching and research interests in Web science, information quality, virtual reality, e-commerce, bioinformatics, and business applications.

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