Multi Dimensional Research Papers - Academia.edu (original) (raw)

Ontologies can represent large, multidimensional spaces: classi- cal music, research in computer science in the UK, health care for breast cancer are examples of rich domains. There have been no easy ways to represent meaningful slices... more

Ontologies can represent large, multidimensional spaces: classi- cal music, research in computer science in the UK, health care for breast cancer are examples of rich domains. There have been no easy ways to represent meaningful slices through these multi- dimensional spaces to privilege the parts of the domain that are of interest to a given user. mSpace, an interaction model

Time is one of the dimensions we frequently find in data warehouses allowing comparisons of data in different periods. In current multi-dimensional data warehouse technology changes of dimension data cannot be represented adequately since... more

Time is one of the dimensions we frequently find in data warehouses allowing comparisons of data in different periods. In current multi-dimensional data warehouse technology changes of dimension data cannot be represented adequately since all dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-dimensional data model employed in data warehouses allowing to cope correctly with changes in dimension data: a temporal multi-dimensional data model allows the registration of temporal versions of dimension data. Mappings are provided to transfer data between different temporal versions of the instances of dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of dimension data.

Purpose The purpose of this paper is to describe the development and validation of a multi-dimensional instrument to measure servant leadership. Design/Methodology/Approach Based on an extensive literature review and expert judgment, 99... more

Purpose The purpose of this paper is to describe the development and validation of a multi-dimensional instrument to measure servant leadership. Design/Methodology/Approach Based on an extensive literature review and expert judgment, 99 items were formulated. In three steps, using eight samples totaling 1571 persons from The Netherlands and the UK with a diverse occupational background, a combined exploratory and confirmatory factor analysis approach was used. This was followed by an analysis of the criterion-related validity. Findings The final result is an eight-dimensional measure of 30 items: the eight dimensions being: standing back, forgiveness, courage, empowerment, accountability, authenticity, humility, and stewardship. The internal consistency of the subscales is good. The results show that the Servant Leadership Survey (SLS) has convergent validity with other leadership measures, and also adds unique elements to the leadership field. Evidence for criterion-related validity came from studies relating the eight dimensions to well-being and performance. Implications With this survey, a valid and reliable instrument to measure the essential elements of servant leadership has been introduced. Originality/Value The SLS is the first measure where the underlying factor structure was developed and confirmed across several field studies in two countries. It can be used in future studies to test the underlying premises of servant leadership theory. The SLS provides a clear picture of the key servant leadership qualities and shows where improvements can be made on the individual and organizational level; as such, it may also offer a valuable starting point for training and leadership development.

Systems like SciScope and Data Access System for Hydrology (DASH) rely on data catalogs to facilitate data discovery. These catalogs describe several nation-wide data repositories that are important for scientists including US Geological... more

Systems like SciScope and Data Access System for Hydrology (DASH) rely on data catalogs to facilitate data discovery. These catalogs describe several nation-wide data repositories that are important for scientists including US Geological Survey's National Water Information System (NWIS), Environmental Protection Agency's STOrage and RETrieval System (EPA STORET) and National Climatic Data Center (NCDC) data collections which contain a wealth

For a very long time, agricultural policy has been interested only in productive or economic aspects. Nevertheless, interventions aiming to support farmers’ income or to promote agricultural modernisation have resulted in several... more

For a very long time, agricultural policy has been interested only in productive or economic aspects. Nevertheless, interventions aiming to support farmers’ income or to promote agricultural modernisation have resulted in several ‘negative’ side-effects, such as increasing pollution, landscape depletion and deepening of regional disparities. Consequently, a need has emerged for confronting problems with a more comprehensive approach, taking into

Large volumes of dynamic stream data pose great challenges to its analysis. Besides its dynamic and transient behavior, stream data has another important characteristic:multi-dimensionality. Much of stream data resides at a... more

Large volumes of dynamic stream data pose great challenges to its analysis. Besides its dynamic and transient behavior, stream data has another important characteristic:multi-dimensionality. Much of stream data resides at a multidimensional space and at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes in some combination of dimensions. To discover high-level dynamic and evolving characteristics, one may need to perform multi-level, multi-dimensional on-line analytical processing (OLAP) of stream data. Such necessity calls for the investigation of new architectures that may facilitate on-line analytical processing of multi-dimensional stream data. In this chapter, we introduce an interestingstream_cubearchitecture that effectively performs on-line partial aggregation of multi-dimensional stream data, captures the essential dynamic and evolving characteristics of data streams, and facilitates fast OLAP on stream data. Three important techniques are proposed for the design and implementation of stream cubes. First, atilted time framemodel is proposed to register time-related data in a multi-resolution model: The more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage requirements of time-related data and adapts nicely to the data analysis tasks commonly encountered in practice. Second, instead of materializing cuboids at all levels, twocriticallayers:observation layerandminimal interesting layer, are maintained to support routine as well as flexible analysis with minimal computation cost. Third, an efficient stream data cubing algorithm is developed that computes only the layers (cuboids) along apopular pathand leaves the other cuboids for on-line, query-driven computation. Based on this design methodology, stream data cube can be constructed and maintained incrementally with reasonable memory space, computation cost, and query response time. This is verified by our substantial performance study. Stream cube architecture facilitates online analytical processing of stream data. It also forms a preliminary structure for online stream mining. The impact of the design and implementation of stream cube in the context of stream mining is also discussed in the chapter.

Socio-economic research on child well-being and the debate around child indicators has evolved quite rapidly in recent decades. An important contribution to this trend is represented by international comparative research based on... more

Socio-economic research on child well-being and the debate around child indicators has evolved quite rapidly in recent decades. An important contribution to this trend is represented by international comparative research based on multi-dimensional child well-being frameworks: most of this research is based on the comparison of average levels of well-being across countries. This paper tries to respond to the complex

PurposeThe purpose of this study is to determine the underlying dimensions of supply chain management (SCM) practices and to empirically test a framework identifying the relationships among SCM practices, operational performance and... more

PurposeThe purpose of this study is to determine the underlying dimensions of supply chain management (SCM) practices and to empirically test a framework identifying the relationships among SCM practices, operational performance and SCM‐related organizational performance with special emphasis on small and medium size enterprises (SMEs) in Turkey.Design/methodology/approachData for the study were collected from a sample of 203 manufacturing SMEs operating in the manufacture of fabricated metal products and general purpose machinery (NACE codes 28 and 29) within the city of Istanbul in Turkey. The research framework was tested using partial least squares method, which is a variance‐based structural equation modeling approach.FindingsBased on exploratory factor analysis (EFA), SCM practices were grouped in two factors: outsourcing and multi‐suppliers (OMS), and strategic collaboration and lean practices (SCLP). The results indicate that both factors of SCLP and OMS have direct positive...

The goal of the 2002-2003 Sandia National Laboratories Computer Science Clinic Project was,to create a tool for simultaneous,visualization of sev- eral different reductions,of multi-dimensional data sets and their analy- sis. Analysis was... more

The goal of the 2002-2003 Sandia National Laboratories Computer Science Clinic Project was,to create a tool for simultaneous,visualization of sev- eral different reductions,of multi-dimensional data sets and their analy- sis. Analysis was done,by implementing,manual,clustering and several au- tomatic clustering algorithms including k-means, linkages, and DBSCAN density. Validity metrics were,implemented,to quantitatively compare,dif- ferent clusterings of the same data, assess the