17608669
Measuring enterprise systems success: the importance of a multiple stakeholder perspective
Organisations invest enormous amount of resources in acquiring Enterprise Systems (ES), presumably expecting positive impacts to the organisation and its functions. Despite the optimistic motives, some ES projects have reported nil or detrimental impacts. This paper studies the proposition that the perspective of the stakeholders of an organisation may have contributed to the differences in receiving benefits reported in prior studies in this domain. An Enterprise System, unlike a traditional Information System (IS), entails many stakeholders ranging from top executives to data entry operators. These stakeholders (a.k.a employment cohorts) typically have multiple and often conflicting objectives and priorities and rarely agree on a set of common aims. The importance of gathering perceptions of success at multiple levels in organizations has been discussed among academics for several decades. However, there is no universal agreement on what employment cohorts should be canvassed (i.e. which are the distinctive employment cohorts?). This paper seeks to illustrate the importance of multiple stakeholder perspective in assessing ES success by analysing 456 responses gathered from 27 public sector organizations. The alleged differences in perceptions of organisational performance are empirically measured using a prior validated model, employing four constructs and thirty-seven measures. The paper identifies three key stakeholder groups in the context of ES and highlights the importance of measuring ES success from a multiple stakeholder view point.
15821497
An Examination Of Barriers To Benefits-realisation From Enterprise Systems In The Public Service
Examination of barriers to benefits-realisation from Enterprise Systems (ES) has potential to provide an innovative perspective on strategic management of large-scale packaged software, and on the extended virtual enterprise explicitly or implicitly deployed across the ES life-cycle. Private sector experience has revealed distinctive conditions applying to ES that create barriers to benefit-realisation. For public service organisations, ES have the potential to enable the provision of end-to-end integrated services, consistent with the demands of public accountability, but are encumbered by a poor track record in large systems implementation and associated process-reengineering. This research analyses the Australian Public Service ES experience, with the aim of dismantling barriers to public sector exploitation of ES. Early results reflect conventional perceptions of implementation issues, but application of the distinctive Dephi-based methodology designed for this study is expected to yield a basis for shared action on the part of government administration, vendors and consultants.
15943063
Enterprise Systems Success: A Measurement Model
This paper presents a validated measurement model and instrument for assessing enterprise systems success from multiple perspectives. The final validated study model employs 27 measures of the four dimensions: information quality, system quality, individual impact, and organizational impact. The model is empirically tested with survey data gathered from 27 public sector organizations that implemented SAP R/3 in the late 1990s. The study consists of an exploratory inventory survey (model building) to identify the salient success dimensions and measures, followed by a confirmatory weights survey, for testing model validity (model testing). Test results demonstrate the discriminant validity of the four dimensions, as well as their convergence on a single higher-order phenomenon: enterprise systems success (ESS). Criterion validity testing further demonstrates the additivity of the four dimensions of success, and the completeness of the resultant overarching, second-order measure of ESS.
6323270
Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs
A conditionally Gaussian channel is a vector channel in which the channel output, given the channel input, has a Gaussian distribution with (well-behaved) input-dependent mean and covariance. We study the capacity-achieving probability measure for conditionally Gaussian channels subject to bounded-input constraints and average cost constraints. Many practical communication systems, including additive Gaussian noise channels, certain optical channels, fading channels, and interference channels fall within this framework. Subject to bounded-input constraint (and average cost constraints), we show that the channel capacity is achievable and we derive a necessary and sufficient condition for a probability measure to be capacity achieving. Under certain conditions, the capacity-achieving measure is proved to be discrete.
18966889
On Levels in Arrangements of Curves 


Abstract. Analyzing the worst-case complexity of the k -level in a planar arrangement of n curves is a fundamental problem in combinatorial geometry. We give the first subquadratic upper bound (roughly O( nk^ 1-1/(9· 2s-3) ) ) for curves that are graphs of polynomial functions of an arbitrary fixed degree s . Previously, nontrivial results were known only for the case s=1 and s=2 . We also improve the earlier bound for pseudo-parabolas (curves that pairwise intersect at most twice) to O( nk7/9 log2/3k) . The proofs are simple and rely on a theorem of Tamaki and Tokuyama on cutting pseudo-parabolas into pseudo-segments, as well as a new observation for cutting pseudo-segments into pieces that can be extended to pseudo-lines. We mention applications to parametric and kinetic minimum spanning trees.

5863029
Euclidean bounded-degree spanning tree ratios
Let <i>t<sub>K</sub></i> be the worst-case (supremum) ratio of the weight of the minimum degree-<i>K</i> spanning tree to the weight of the minimum spanning tree, over all finite point sets in the Euclidean plane. It is known that <i>t<sub>2</sub>=2</i> and <i>t<sub>5</sub>=1</i>. In STOC'94, Khuller, Raghavachari, and Young established the following inequalities: <i>1.103<t<sub>3</sub>= 1.5</i> and <i>1.035<t<sub>4</sub>= 1.25</i>. We present the first improved upper bounds: <i>t<sub>3</sub> < 1.402</i> and <i>t<sub>4</sub> < 1.143</i>. As a result, we obtain better approximation algorithms for Euclidean minimum bounded-degree spanning trees.Let <i>t<sub>K</sub><sup>(d)</sup></i> be the analogous ratio in <i>d</i>-dimensional space. Khuller et al. showed that <i>t<sub>3</sub><sup>(d)</sup><1.667</i> for any <i>d</i>. We observe that <i>t<sub>3</sub><sup>(d)</sup><1.633</i>.
45840953
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. In higher dimension, the boundaries become wiggly and over-fitting often occurs. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But as clusters touch each other, CIFs still clearly reveal cluster centers, cluster boundaries, and, degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on kernel density functions which are often oscillatory or over-smoothed. These problems of kernel density estimation are resolved using Level Set Methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented to illustrate the advantages of our approach.
13840611
Dynamic cluster formation using level set methods
Density-based clustering has the advantages for: 1) allowing arbitrary shape of cluster and 2) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But, when clusters become closed to each other, CIFs still clearly reveal cluster centers, cluster boundaries, and degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on density functions obtained by kernel density estimation, which are often oscillatory or oversmoothed. These problems of kernel density estimation are resolved using level set methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented which illustrate the advantages of our approach.