ERP AND E-BUSINESS

The Internet has revolutionized twenty-first century business. Organizations today can communicate with customers, suppliers, and sellers at e-speed with the click of a mouse. Yet, with all of the excitement about the external possibilities of the Internet, companies still need efficient internal processes to make and move products, manage finances, recruit and motivate employees, and excel. The companies best positioned to succeed in the near future are those that can balance existing enterprise resource planning (ERP)-based infrastructures and capabilities with exciting new e-business innovations. This paper elaborates the issues of ERP and e-business.ERP, E-Business, network, enterprise, management.

Leveraging Technology towards HR Excellence

The last decade has seen a significant increase in the number of organizations gathering, storing and analyzing human resources data using Human Resource Information Systems (HRIS) (Ball, 2001; Barron et al., 2004; Hussain et al., 2007; Ngai & Wat, 2006). HR to benefit almost every realm of business operations is leveraging technology. Many technology applications support Human Resource functions like erecruitment, employee tracking system, performance management software, e earning mode etc are helping dramatically to improve HR’s ability to carry out effective talent management and get better employer brand. Integration of technology and HR not only mean harmonious co-existing but would also mean the journey towards business excellence. This paper explores the understanding on positive impact of Human Resource Information System (HRIS) currently in use for people management with perception on Information Communication Technology (ICT) applications adopted by many industries. The study follows qualitative research as its plan of action in gathering information. The information gathered in crosschecked with industry to cross check the reliability

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature

A Feature-Oriented Software Engineering Approach to Integrate ASSISTments with Learning Management Systems

Object-Oriented Programming (OOP), in the past two decades, has become the most influential and dominant programming paradigm for developing large and complex software systems. With OOP, developers can rely on design patterns that are widely accepted as solutions for recurring problems and used to develop flexible, reusable and modular software. However, recent studies have shown that Objected-Oriented Abstractions are not able to modularize these pattern concerns and tend to lead to programs with poor modularity. Feature-Oriented Programming (FOP) is an extension of OOP that aims to improve the modularity and to support software variability in OOP by refining classes and methods. In this thesis, based upon the work of integrating an online tutor systems, ASSISTments, with other online learning management systems, we evaluate FOP with respect to modularity. This proof-of-concept effort demonstrates how to reduce the effort in designing integration code

Examining the Organizational Decision to Adopt Service-Oriented Architecture (SOA) - Development of a Research Model

What are the determinants of an organization’s decision to adopt Service-Oriented Architecture (SOA)? Although the paradigms of service orientation and SOA have become quite omnipresent in the IS literature, research is still lacking to provide a comprehensive view upon drivers and inhibitors of the organizational decision to adopt SOA. Based on the mature strand of adoption research, this paper develops a conceptual model in order to increase the understanding of the determinants influencing this decision. Thereby, the drivers and inhibitors are distinguished in organization-specific and innovation-specific factors. The organization-specific factors cover two aspects: (1) the compatibility of technology and organization (i.e., SOA expertise of the employees, management support for SOA, IT/ Business alignment, degree of process documentation) and (2) management fad and fashion. The innovation-specific factors cover the perceived benefits, perceived complexity, and standardization of available technologies related to SOA. Beside developing this theoretical model for laying the foundation for future empirical research, a further contribution of this paper is the development of a comprehensive measurement model for SOA adoption, which differentiates between the IT and the enterprise layer

A FRAMEWORK FOR ARCHITECTURAL HERITAGE HBIM SEMANTIZATION AND DEVELOPMENT

Despite the recognized advantages of the use of BIM in the field of architecture and engineering, the extension of this procedure to the architectural heritage is neither immediate nor critical. The uniqueness and irregularity of historical architecture, on the one hand, and the great quantity of information necessary for the knowledge of architectural heritage, on the other, require appropriate reflections. The aim of this paper is to define a general framework for the use of BIM procedures for architectural heritage. The proposed methodology consists of three different Level of Development (LoD), depending on the characteristics of the building and the objectives of the study: a simplified model with a low geometric accuracy and a minimum quantity of information (LoD 200); a model nearer to the reality but, however, with a high deviation between virtual and real model (LoD 300); a detailed BIM model that reproduce as much as possible the geometric irregularities of the building and is enriched by the maximum quantity of information available (LoD 400)

Databases Lie! Successfully Managing Structured Data, the Oft-Overlooked ESI

Legal professionals regularly advise clients to ensure that the storage, retention, and accessibility of their Electronically-Stored Information (“ESI”) is in full compliance with all legal and regulatory requirements in the event this information becomes relevant in civil, criminal, or regulatory disputes. However, what many practitioners may not realize is that the ESI that clients are required to produce for e- discovery includes both “unstructured” and “structured” data. Searching and producing only one of these types of ESI may well not fully satisfy a client’s full discovery obligations. Even worse, it might not present a full understanding of the factual issues in the matter and how to best prove them to the legal team