A tudásmenedzsment és az IT audit kapcsolódási pontjai - a tudásmenedzsment-rendszerek auditja (Connection points of knowledge management and IT audit – audit of knowledge management systems)

A cikkben a szerzők megvizsgálják a tudásmenedzsment komplex rendszerfejlesztési projektekben és az informatikai auditban játszott szerepét. Fő céljuk, hogy a tudásmenedzsment-rendszerek fejlesztéséhez kapcsolódó audit támogatására értékelési modellt készítsenek. Cikkükben megvizsgálják a tudásmenedzsmentnek az IT-auditban játszott általános szerepét, az auditban érintett tudásvagyon védelmének kérdését, a tudásmenedzsment-folyamatok szerepét a rendszerfejlesztésben (auditszempontból), a kontrollok implementálását, valamint a tudásmenedzsment és az IT-audittal kapcsolatos szabványok, módszertanok kapcsolatát. Az eredmények illusztrálására egy az Európai Unió 7. keretprogramjából finanszírozott nemzetközi projekt (GUIDE, IST–2003–507498) szolgál. ________________ Authors investigate the role of knowledge management in complex system development projects and IT audit. The primary goal is to provide an evaluation framework for an assessment of the development of special knowledge management solutions. On the other hand IT audit itself is a knowledge-dependent activity. The paper analyses the role of knowledge management in IT audit in general, the protection of knowledge assets during an audit, the role of knowledge management processes during system development (from audit point of view) and in the implementation of controls, the relationship of knowledge management with audit standards. Authors investigate the specialities of KM developments from audit point of view (particularly important aspects of audit, specific control objectives) A case study, based on experiences gained from GUIDE project (IST-2003-507498 funded by the European Commission’s 6th Framework Programme) illustrates the findings.

Evaluation of corporate environmental management approaches: A framework and application

This article proposes a framework to evaluate corporate environmental strategies. In the proposed framework, a company's environmental risks are analyzed on two dimensions, One dimension, the endogenous environmental risks, arises from the internal operations of the company. The other dimension, the exogenous environmental risks, are determined by the company's external world: its location, its ecological setting, and the demographic characteristics of the physical environment in which it operates. Four environmental management approaches are defined as a function of endogenous and exogenous environmental risks: reactive, proactive, strategic, and crisis preventive. The framework was applied in a survey of 141 company representatives in Hungary. A relationship was sought between the a priori defined environmental management approaches based on technology and location and the companies' environmental management characteristics defined by senior managers. Variables that differentiated among the four environmental management approaches were identified and ranked. The study concludes that there is a relatively well-defined relationship between the environmental risks of companies and the nature of their environmental management approaches, Implementing a strategic environmental management approach may not be the best option for all companies - although there is a growing pressure to do so.

Design principles of semantic binary database management systems

The Semantic Binary Data Model (SBM) is a viable alternative to the now-dominant relational data model. SBM would be especially advantageous for applications dealing with complex interrelated networks of objects provided that a robust efficient implementation can be achieved. This dissertation presents an implementation design method for SBM, algorithms, and their analytical and empirical evaluation. Our method allows building a robust and flexible database engine with a wider applicability range and improved performance. ^ Extensions to SBM are introduced and an implementation of these extensions is proposed that allows the database engine to efficiently support applications with a predefined set of queries. A New Record data structure is proposed. Trade-offs of employing Fact, Record and Bitmap Data structures for storing information in a semantic database are analyzed. ^ A clustering ID distribution algorithm and an efficient algorithm for object ID encoding are proposed. Mapping to an XML data model is analyzed and a new XML-based XSDL language facilitating interoperability of the system is defined. Solutions to issues associated with making the database engine multi-platform are presented. An improvement to the atomic update algorithm suitable for certain scenarios of database recovery is proposed. ^ Specific guidelines are devised for implementing a robust and well-performing database engine based on the extended Semantic Data Model. ^

Examining Technology Adoption and Management Perception of Inventory Management Systems: The Case of Aruba Restaurants

The purpose of this paper is to explore the use of automated inventory management systems (IMS) and identify the stage of technology adoption for restaurants in Aruba. A case study analysis involving twelve members of the Aruba Gastronomic Association was conducted using a qualitative research design to gather information on approaches currently used as well as the reasons and perceptions managers/owners have for using or not using automated systems in their facilities. This is the first study conducted using the Aruba restaurant market. Therefore, the application of two technology adoption models was used to integrate critical factors relevant to the study. Major findings indicated the use of an automated IMS in restaurants is limited, thus underscoring the lack of adoption of technology in this area. The results also indicated that two major reasons that restaurants are not adopting IMS technology are budgetary constraints and service support. This study is imperative for two reasons: (1) the results of this study can be used as a comparison for future IMS adoption, not only for Aruba’s restaurant industry but also for other Caribbean destinations and the U.S., (2) this study also provides insight into the additional training and support help needed in hospitality technology services.

Shopping for Computer Restaurant Management Systems

A myriad of computer management systems are available for the restaurant business. The author discusses all aspects of evaluating, purchasing, and using such systems for a restaurant operation.

Evaluating the Success of Lodging Yield Management Systems

Although there are more than 7,000 properties using lodging yield management systems (LYMSs), both practitioners and researchers alike have found it difficult to measure their success. Considerable research was performed in the 1980s to develop success measures for information systems in general. In this work the author develops success measures specifically for LYMSs.

Taxonomy of Institutional Issues that Act as Barriers to the Implementation of Pavement Management Systems, 1994

The benefits of pavement management system when fully implemented are well known and the history of successful implementation is rich. Implementation occurs, for purposes of this paper, when the pavement management system is the critical component for making pavement decisions. This paper addresses the issues that act as barriers to full implementation of pavement management systems. Institutional barriers, not technical and financial barriers, are more commonly responsible for a pavement management systems falling short of full implementation. The paper groups these institutional issues into a general taxonomy. In general, more effort needs to be put forth by highway agencies to overcome institutional issues. Most agencies approach pavement management as a technical process, but more commonly, institutional issues become more problematic and thus require more attention paid to institutional issues. The paper concludes by summarizing the implementation process being taken by the Iowa Department of Transportation. The process was designed to overcome institutional barriers and facilitate the complete and full implementation of their pavement management system.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda

Information Technology (IT) can be an important component for innovation since enabling e-learning it can provide conditions to which the organization can work with new business and improved processes. In this regard, the Learning Management Systems (LMS) allows communication and interaction between teachers and students in virtual spaces. However the literature indicates that there are gaps in the researches, especially concerning the use of IT for the management of e-learning. The purpose of this paper is to analyze the available literature about the application of LMS for the e-learning management, seeking to present possibilities for researches in the field. An integrative literature review was performed considering the Web of Science, Scopus, Ebsco and Scielo databases, where 78 references were found, of which 25 were full papers. This analysis derives interesting characteristics from scientific studies, highlighting gaps and guidelines for future research.

A Belief rule-based environmental responsibility assessment system for small and medium-sized enterprises

Various environmental management systems, standards and tools are being created to assist companies to become more environmental friendly. However, not all the enterprises have adopted environmental policies in the same scale and range. Additionally, there is no existing guide to help them determine their level of environmental responsibility and subsequently, provide support to enable them to move forward towards environmental responsibility excellence. This research proposes the use of a Belief Rule-Based approach to assess an enterprise’s level commitment to environmental issues. The Environmental Responsibility BRB assessment system has been developed for this research. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses (using the Belief Rule-Based approach) will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB systems consist of two parts: Knowledge Base and Inference Engine. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining, where an inference starts iteratively searching for a pattern-match of the input and if-then clause. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.

A study on energy and environmental management techniques used in petroleum process industries

Petroleum process industries are one of the most energy and emission intensive sectors throughout the world.There are natural gas processing plant, crude oils and condensate fractionation plant, liquefied natural gas plantand liquefied petroleum gas plant etc. creates environmental pollution by processing and handling of petroleumproducts. The study critically reviewed and discussed the energy and environmental management includingpollution control of petroleum process industries of Bangladesh. They produce both gaseous (process gas, wastegas etc.) and liquid (produced water, waste oil and grease etc.) pollutants. The study found that the liquid pollutantlike waste water is more hazardous and its treatment process is highly complicated due to its higher salinity, morecorrosivity and grease contain characteristics. As part of energy management, the rational use of energy and energyflow diagram of the petroleum industry is presented. Finally, a time frame measures which can be implemented inorder to save energy is outlined. The study concluded that the rational use of energy and proper environmentalmanagement are essential for achieving energy and environmental sustainability of process industries.

Mitigating malicious feedback attacks in trust management systems

In electronic commerce (e-commerce) environment, trust management has been identified as vital component for establishing and maintaining successful relational exchanges between the trading partners. As trust management systems depend on the feedbacks provided by the trading partners, they are fallible to strategic manipulation of the rating attacks. Therefore, in order to improve the reliability of the trust management systems, an approach that addresses feedback-related vulnerabilities is paramount. This paper proposes an approach for identifying and actioning of falsified feedbacks to make trust management systems robust against rating manipulation attacks. The viability of the proposed approach is studied experimentally and the results of various simulation experiments show that the proposed approach can be highly effective in identifying falsified feedbacks.

Critical success factors for implementing risk management systems in developing countries

A review of published studies on risk management in developing countries reveals that critical success factors for implementing risk management has remained an under-researched area of investigation. This paper is aimed at investigating the perceptions of construction professionals concerning the critical success factors (CSFs) for implementation of risk management systems (IRMS). Survey data was collected from 87 construction professionals from the Iranian construction industry as a developing country. The results indicate that four factors are regarded as highly critical: ‘support from managers’, ‘inclusion of risk management in construction education and training courses for construction practitioners’, ‘attempting to deliver projects systematically’, and ‘awareness and knowledge of the process for implementing risk management’. Assessing the associations among CSFs also highlighted the crucial role of enhancing the effectiveness of knowledge management practices in construction organisations. Study also revealed that parties involved in projects do not agree on the level of importance of CSFs for implementing risk management in developing countries. This study contributes to practice and research in several ways. For practice, it increases understanding of how closely knowledge management is associated with the implementation of risk management systems in developing countries. For research, the findings would encourage construction practitioners to support effective knowledge management as a precursor to higher levels of risk management implementation on construction projects.

Entrepreneurship in project management systems: proposal of a model and preliminary empirical evidence

This study analyzes the manifestation of the dimensions of Entrepreneurial Orientation (EO) and Project Management Systems (PMS). We used a qualitative approach to conduct exploratory research through a study in literature and a pilot case in a software company. Data was collected from semi structured interviews, documents, and records on file, then triangulated and treated with content analysis. The model proposed for the relationship between the types of PMS (ad hoc, Classic PM, innovation, entrepreneurship/intrapreneurship) and the dimensions of EO (innovativeness, risk-taking, proactiveness, competitive aggressiveness, and autonomy), was partially corroborated by empirical studies. New studies are suggested to validate the applicability and setup of the model.