The Importance Of End-User Analysis In New Information System Adapters: Lessons Learned From Practice

The implementation of Information System (IS) in new-IS adapters can remain unused even when they developed properly. The previous research shows that the unsuccessful IS utilization problem primarily related to behavioral issues rather than technical issues. The behavioral issues should be addressed in the stakeholder analysis, an activity in the inception phase of requirement engineering. End-users of an IS are one of the focuses in stakeholder analysis. We studied the correlation of the end-user analysis in new-IS adapters with the successfulness of IS utilization. We conducted a qualitative studied on 20 IS development projects. The findings show strong correlation of the end-user behavior and the IS adoption. We suggest the end-user analysis is necessary for the IS development project in new-IS adapters. We concluded that it is recommended to formally get the end-user commitment before starting the IS development process. Keyword : Information System, end-user analysis, new-IS adapters, end-user commitmen

An Approach for Automatically Generate Star Schema from Natural Language

The star schema is a form of data warehouse modelling, which acts primary storage for dimensional data that enables efficient retrieval of business information for decision making. Star schemas can be generated from business needs that we refer to as a user business key or from a relational schema of the operational system. There are many tools available to automatically generate star schema from relational schema, such as BIRST and SAMSTAR; however, there is no application that can automatically generate it from a user business key that is represented in the form of human language. In this paper, we offered an approach for automatically generating star schema from user business key(s). It begins by processing the user business key using a syntactical parsing process to identify noun words. Those identified words will be used to generate dimension table candidates and a fact table. The evaluation result indicates that the tool can generate star schema based on the inputted user business key(s) with some limitations in that the star schema will not be formed if the dimensional tables do not have a direct relationship

Ontology-guided job market demand analysis: A cross-sectional study for the data science field

The rapid changes in the job market, including a continuous year-on-year increase in new skills in sectors like information technology, has resulted in new challenges for job seekers and educators alike. The former feel less informed about which skills they should acquire to raise their competitiveness, whereas the latter are inadequately prepared to offer courses that meet the expectations by fast-evolving sectors like data science. In this paper, we describe efforts to obtain job demand data and employ a information extraction method guided by a purposely-designed vocabulary to identify skills requested by the job vacancies. The Ontology-based Information Extraction (OBIE) method employed relies on the Skills and Recruitment Ontology (SARO), which we developed to represent job postings in the context of skills and competencies needed to fill a job role. Skill demand by employers is then abstracted using co-word analysis based on a set of skill keywords and their co-occurrences in the job posts. This method reveals the technical skills in demand together with their structure for revealing significant linkages. In an evaluation, the performance of the OBIE method for automatic skill annotation is estimated (strict F-measure) at 79%, which is satisfactory given that human inter-annotator agreement was found to be automatic keyword indexing with an overall strict F-measure at 94%. In a secondary study, sample skill maps generated from the matrix of co-occurrences and correlation are presented and discussed as proof-of-concept, highlighting the potential of using the extracted OBIE data for more advanced analysis that we plan as future work, including time series analysis