scholarly journals Business Intelligence Infrastructure for Academic Libraries

2013 ◽  
Vol 8 (2) ◽  
pp. 172 ◽  
Author(s):  
Joe Zucca
Keyword(s):  
Business Intelligence ◽  
Resource Sharing ◽  
User Behavior ◽  
Quantitative Information ◽  
Data Sources ◽  
Activity Data ◽  
Extensible Markup ◽  
Binary Object ◽  
The University ◽  
Transactional Data

Objective – To describe the rationale for and development of MetriDoc, an information technology infrastructure that facilitates the collection, transport, and use of library activity data. Methods – With the help of the Institute for Museum and Library Services, the University of Pennsylvania Libraries have been working on creating a decision support system for library activity data. MetriDoc is a means of “lighting up” an array of data sources to build a comprehensive repository of quantitative information about services and user behavior. A data source can be a database, text file, Extensible Markup Language (XML), or any binary object that contains data and has business value. MetriDoc provides simple tools to extract useful information from various data sources; transform, resolve, and consolidate that data; and finally store them in a repository. Results – The Penn Libraries completed five reference projects to prove basic concepts of the MetriDoc framework and make available a set of applications that other institutions could test in a deployment of the MetriDoc core. These reference projects are written as configurable plugins to the core framework and can be used to parse and store EZ-Proxy log data, COUNTER data, interlibrary loan transactional data from ILLIAD, fund expenditure data from the Voyager integrated library system, and transactional data from the Relais platform, which supports the BorrowDirect and EZBorrow resource sharing consortiums. The MetriDoc framework is currently undergoing test implementations at the University of Chicago and North Carolina State University, and the Kuali-OLE project is actively considering it as the basis of an analytics module. Conclusion – If libraries decide that a business intelligence infrastructure is strategically important, deep collaboration will be essential to progress, given the costs and complexity of the challenge.

CARL Libraries – a Canadian resource-sharing experience

2015 ◽  
Vol 43 (1) ◽  
pp. 22-33 ◽  
Author(s):  
C J de Jong ◽  
Linda J Frederiksen
Keyword(s):  
Resource Sharing ◽  
Current Trend ◽  
Annual Data ◽  
Content Type ◽  
University Of Alberta ◽  
Research Libraries ◽  
Canadian Association ◽  
Sharing Patterns ◽  
The University ◽  
Transactional Data

Purpose – This study aims to map the current resource-sharing environment in Canada through the lens of its research libraries in general and the University of Alberta in particular. The findings present an interesting view of changing resource sharing patterns and trends. Design/methodology/approach – Interlibrary loan (ILL) transaction data were compiled from annual data reported to the Canadian Association of Research Libraries (CARL) and a case study of the University of Alberta is presented. Findings – The current trend shows declines in both borrowing and lending transactions. Research limitations/implications – Validity of the CARL ILL transactional data is subject to consistency in institutional reporting and accuracy of the data. The trends portrayed in the data are deemed realistic of the Canadian experience. Originality/value – This is an original study of CARL ILL transactional data, providing an aggregated view of 13 years of annual data, and an analysis of this data. It updates previous research and benchmarks current ILL patterns at CARL institutions.

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

scholarly journals Library Impact Practice Brief: Supporting Bibliometric Data Needs at Academic Institutions

2020 ◽  
Author(s):  
Shannon Gordon ◽  
Alison Hitchens
Keyword(s):  
Service Providers ◽  
Lessons Learned ◽  
Data Sources ◽  
Campus Community ◽  
Bibliometric Data ◽  
Institutional Level ◽  
Research Libraries ◽  
Library Impact ◽  
The University ◽  
Impact Framework

This practice brief presents research conducted by staff at the University of Waterloo Library as part of the library’s participation in ARL’s Research Library Impact Framework initiative. The research addressed the question, “How can research libraries support their campus community in accessing needed bibliometric data for institutional-level purposes?” The brief explores: service background, partners, service providers and users, how bibliometric data are used, data sources, key lessons learned, and recommended resources.

scholarly journals Building a Medical Education Outcomes Center: Development Study

10.2196/14651 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e14651
Author(s):  
Mark E Rosenberg ◽  
Jacqueline L Gauer ◽  
Barbara Smith ◽  
Austin Calhoun ◽  
Andrew P J Olson ◽  
...  
Keyword(s):  
Medical Education ◽  
Medical School ◽  
Patient Outcomes ◽  
Data Sources ◽  
University Of Minnesota ◽  
Education Outcomes ◽  
Learner Outcomes ◽  
Education Data ◽  
The University ◽  
Training Practice

Background Medical education outcomes and clinical data exist in multiple unconnected databases, resulting in 3 problems: (1) it is difficult to connect learner outcomes with patient outcomes, (2) learners cannot be easily tracked over time through the education-training-practice continuum, and (3) no standard methodology ensures quality and privacy of the data. Objective The purpose of this study was to develop a Medical Education Outcomes Center (MEOC) to integrate education data and to build a framework to standardize the intake and processing of requests for using these data. Methods An inventory of over 100 data sources owned or utilized by the medical school was conducted, and nearly 2 dozen of these data sources have been vetted and integrated into the MEOC. In addition, the American Medical Association (AMA) Physician Masterfile data of the University of Minnesota Medical School (UMMS) graduates were linked to the data from the National Provider Identifier (NPI) registry to develop a mechanism to connect alumni practice data to education data. Results Over 160 data requests have been fulfilled, culminating in a range of outcomes analyses, including support of accreditation efforts. The MEOC received data on 13,092 UMMS graduates in the AMA Physician Masterfile and could link 10,443 with NPI numbers and began to explore their practice demographics. The technical and operational work to expand the MEOC continues. Next steps are to link the educational data to the clinical practice data through NPI numbers to assess the effectiveness of our medical education programs by the clinical outcomes of our graduates. Conclusions The MEOC provides a replicable framework to allow other schools to more effectively operate their programs and drive innovation.

scholarly journals A Modern Perspective on Phishing: An investigation into susceptibility to phishing attacks between mobile and desktop email clients

2021 ◽  
Author(s):  
◽  
Jayden Nowitz
Keyword(s):  
Mobile Device ◽  
User Behavior ◽  
Administrative Staff ◽  
Second Stage ◽  
Significant Difference ◽  
Device Type ◽  
Group A ◽  
Group B ◽  
The University ◽  
Modern Perspective

<p>Research on how to counter phishing from a user behavior perspective has been explored for over a decade, yet the prevalence of such threats is increasing. This thesis aims to provide a modern perspective by considering if there is a difference in how susceptible an individual is on a mobile device versus a desktop email client. Currently very few studies consider phishing on mobile devices and the research is unclear as to the potential difference in susceptibility rates between the two device types. Initially a review of 60 phishing emails received by the university that had passed mail filtering were used to assist in the design of the messages to be used in the second stage of the study. Following this a simulated phishing attack on two groups in one unit of professional administrative staff in the university (141 in total with 71 in Group A and 70 in Group B) within the university was undertaken. The defining characteristic between the groups was how they responded to a message with a ‘loss versus gain’ appeal. This area has received limited exploration in the research and findings remain unclear. This study found that people were statistically far more susceptible to the ‘gain’ message of a free coffee at 28.2% than the ‘loss’ message of Office365 account suspension 7.1% . For device type there appears to be no statistically significant difference, even between the groups. This study highlights the complexities of device usage around phishing, that have not been clearly highlighted in previous studies, such as people viewing emails with one device and falling victim on another device.</p>

Suggested Model for Business Intelligence in Higher Education

Data Mining ◽  
2013 ◽  
pp. 550-566 ◽  
Author(s):  
Zaidoun Alzoabi ◽  
Faek Diko ◽  
Saiid Hanna
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Business Intelligence ◽  
University Management ◽  
Suggested Model ◽  
Business Decision ◽  
Data Mining Approach ◽  
Input Processing ◽  
The University

BI is playing a major role in achieving competitive advantage in almost every sector of the market, and the higher education sector is no exception. Universities, in general, maintain huge databases comprising data of students, human resources, researches, facilities, and others. Data in these databases may contain decisive information for decision making. In this chapter we will describe a data mining approach as one of the business intelligence methodologies for possible use in higher education. The importance of the model arises from the reality that it starts from a system approach to university management, looking at the university as input, processing, output, and feedback, and then applies different business intelligence tools and methods to every part of the system in order to enhance the business decision making process. The chapter also shows an application of the suggested model on a real case study at the Arab International University.

LMS Adoption at the University of Genova

2012 ◽  
pp. 271-291
Author(s):  
Maura Cerioli ◽  
Marina Ribaudo ◽  
Marina Rui
Keyword(s):  
Instructional Design ◽  
Organizational Support ◽  
Resource Sharing ◽  
Degree Program ◽  
Traditional Teaching ◽  
Teaching Process ◽  
Network Technologies ◽  
The University ◽  
The Impact

The last two decades have seen the spread of LMS among schools, universities, and companies to augment the traditional teaching process with ICT and network technologies. This chapter presents the process leading to the adoption of a Moodle based LMS at the University of Genova in the last decade. By analyzing the data collected from the LMS logs and from questionnaires proposed both to students and teachers, we found out that the needs of the stakeholders are largely limited to resource sharing and organizational support, satisfactorily provided by the current service. Further improvements could be achieved by the introduction of a policy encouraging or forcing the teachers to use the provided LMS. A project on instructional design and, as a case study, the evolution of some of the courses involved in it are also presented. Though the redesign of such courses has improved their results, the impact on the overall organization of the degree program has been negative. We infer that this is due to the excessive freedom the students enjoy in taking their exams in Italy.

scholarly journals A high-resolution regional emission inventory of atmospheric mercury and its comparison with multi-scale inventories: a case study of Jiangsu, China

2016 ◽  
Author(s):  
Hui Zhong ◽  
Yu Zhao ◽  
Marilena Muntean ◽  
Lei Zhang ◽  
Jie Zhang
Keyword(s):  
High Resolution ◽  
Coal Combustion ◽  
Raw Materials ◽  
Field Measurements ◽  
Atmospheric Mercury ◽  
Data Sources ◽  
Activity Levels ◽  
Air Pollution Control ◽  
Activity Data ◽  
Multi Scale

Abstract. A better understanding of the discrepancies in multi-scale inventories could give an insight on their approaches and limitations, and provide indications for further improvements; international, national and plant-by-plant data sources are primarily obtained to compile those inventories. In this study we develop a high-resolution inventory of Hg emissions at 0.05° × 0.05° for Jiangsu China using a bottom-up approach and then compare the results with available global/national inventories. With detailed information on individual sources and the updated emission factors from field measurements incorporated, the annual Hg emissions of anthropogenic origin in Jiangsu 2010 are estimated at 39 105 kg, of which 51 %, 47 % and 2 % were released as Hg0, Hg2+, and HgP, respectively. This provincial inventory is thoroughly compared to the downscaled results from three national inventories (NJU, THU and BNU) and two global inventories (AMAP/UNEP and EDGARv4.tox2). Attributed to varied methods and data sources, clear information gaps exist in multi-scale inventories, leading to differences in the emission levels, speciation and spatial distributions of atmospheric Hg. The total emissions in the provincial inventory are the largest, i.e., 28 %, 7 %, 19 %, 22 %, and 70 % higher than NJU, THU, BNU, AMAP/UNEP, and EDGARv4.tox2, respectively. For major sectors including power generation, cement, iron &amp; steel and other coal combustion, the Hg contents (HgC) in coals/raw materials, abatement rates of air pollution control devices (APCD) and activity levels are identified as the crucial parameters responsible for the differences in estimated emissions between inventories. Regarding speciated emissions, larger fraction of Hg2+ is found in the provincial inventory than national and global inventories, resulting mainly from the results by the most recent domestic studies in which enhanced Hg2+ were measured for cement and iron &amp; steel plants. Inconsistent information of big power and industrial plants is the main source of differences in spatial distribution of emissions between the provincial and other inventories, particularly in southern and northwestern Jiangsu where intensive coal combustion and industry are located. Quantified with Monte-Carlo simulation, uncertainties of provincial Hg emissions are smaller than those of NJU national inventory, resulting mainly from the more accurate activity data of individual plants and the reduced uncertainties of HgC in coals/raw materials.

Intelligent Analytics

2011 ◽  
Vol 2 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Lakshmi S. Iyer ◽  
Rajeshwari M. Raman
Keyword(s):  
Business Intelligence ◽  
New Technology ◽  
Customer Relationship ◽  
Web Analytics ◽  
Web Usage ◽  
Emerging Trends ◽  
Key Players ◽  
Transactional Data ◽  
Technology Trends ◽  
Usage Data

Organizations use web analytic tools and technologies to measure, collect, analyze, and report web usage data to help optimize websites. Traditionally, most of this data tends to be non-transactional and non-identifiable. In this regard, there has not been much integration with transactional data that is collected, stored, analyzed, and reported through Business Intelligence (BI). Emerging trends in web analytics provide organizations the ability to aggregate and analyze web analytics data with transactional data to provide valuable insights for building better customer relationship strategies. In this paper, the authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives. While the benefits are significant, there are some challenges associated with the integration and a few possible solutions to address.

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