ON INTEGRATION OF EVOLVING INFRASTRUCTURE TOPOLOGY GRAPHS AND METRIC DATA STREAMS IN INFORMATION TECHNOLOGY INFRASTRUCTURE MANAGEMENT

Modern cloud-based information technology (IT) infrastructure monitoring context and data are gathered from various systems. Typical monitoring systems provide a set of metrics characterizing the performance and health of a variety of infrastructure components. To understand the dependencies and relations among these measurements, the infrastructure topology can be analysed to provide context to the monitoring metrics. However, the metrics and the topology are updated at different time intervals and providing continuous merging and analysis of both data sets is a challenging task which is rarely addressed in the scientific literature. The paper elaborates a method for integration of infrastructure topology graph and monitoring metric data streams. The method is intended for application in the identification of anomalies in IT infrastructure.

Capability driven development: an approach to designing digital enterprises

The final publication is available at Springer via http://dx.doi.org/10.1007/s12599-014-0362-0[EN] The need for organizations to operate in changing environments is addressed by proposing an approach that integrates organizational development with information system (IS) development taking into account changes in the application context of the solution. This is referred to as Capability Driven Development (CDD). A meta-model representing business and IS designs consisting of goals, key performance indicators, capabilities, context and capability delivery patterns, is being proposed. The use of the meta-model is validated in three industrial case studies as part of an ongoing collaboration project, whereas one case is presented in the paper. Issues related to the use of the CDD approach, namely, CDD methodology and tool support are also discussed.This work has been partially supported by the EU-FP7 funded project no: 611351 CaaS - Capability as a Service in Digital Enterprises.Berzisa, S.; Bravos, G.; Cardona Gonzalez, T.; Czubayko, U.; España, S.; Grabis, J.; Henkel, M.... (2015). Capability driven development: an approach to designing digital enterprises. Business and Information Systems Engineering. 57(1):15-25. https://doi.org/10.1007/s12599-014-0362-0S1525571ArchiMate (2013) An enterprise modeling language from the Open Group. http://www.opengroup.org/archimate/ . Accessed 3 Dec 2014Asadi M, Ramsin R (2008) MDA-based methodologies: an analytical survey. In: Proceedings Model driven architecture – foundations and applications (ECMDA-FA 2008), LNCS 5095, pp 419–431Barney JB (1991) Firm resources and sustained competitive advantage. 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Joint Optimization of Physical and Information Flows in Supply Chains

Supply chain units are connected by both physical and in- formation ows. Electronic services are used to perform a large part of activities in modern supply chains. The traditional supply chain cong- uration models pay the main attention to optimization of the physical ows. However, it is equally important to ensure that the physical units have appropriate information processing and logistics capabilities. There- fore, a model for joint conguration of the physical and information ows in e-retailing supply chains is elaborated in this paper. The model simul- taneously identies appropriate suppliers, selects the third party logistics service provider and selects appropriate electronic information process- ing services. The typical services used in global e-retailing are identied. The services are characterized by their functionality and quality of ser- vice measurements. The preliminary experimental studies demonstrate interdependencies between the physical and information ows

Spatial Data Integration Approach with Application in Facility Location

Complex decision-making problem, require large amount of data, and data gathering is one of the most complex tasks in the modeling process. Web services and XML are among the most promising technologies for retrieving data from distributed data sources over the Internet. However, different standards are used for providing spatial and non-spatial data, and data retrieval quality of service attributes vary substantially. The objective of this paper is to propose a spatial data integration architecture, which supports data retrieval from heterogeneous, distributed data sources and accounts for quality of service requirements. Application of the integration architecture is demonstrated using a facility location decision-making problem, which requires such spatial data as distances, customer densities and location of competitors. Computational studies are conducted to evaluate data retrieval time and to identify hidden characteristics of different data sources

On-demand Data Integration for Decision-making Applications

Decision-making efficiency depends upon timely availability of appropriate data. On-demand data integration from web based data sources provides an attractive solution to data gathering. The two major challenges associate with on-demand data integration are selection of appropriate data services and efficient implementation of the data integration process. In this paper, it is proposed to use service’s business value as a service selection criterion and to elaborate a lightweight method for XML based definition of the integration processes. The business value driven approach implies that services are selected according to their impact on quality of decisions made rather than solely according to their QoS characteristics. The data integration process definition methods partitions the data integration process into atomic data integration tasks, thus allowing for high level of data retrieval parallelization, accommodating data interdependencies and enabling error recoverability without delaying the whole data integration process. The data integration methods are evaluated using a case study, which investigates on-line decision making at a taxi company