Saving Space and Time Using Index Merging

Managing digital information is an integral part of our society. Efficient access to data is supported through the use of indices. Although indices can reduce the cost of answering queries, they have two significant drawbacks: they take additional storage space and their maintenance can become a bottleneck. We address these challenges by introducing search data structures that reduce the need for storing redundant data among indices. Our experimental results with the main-memory version of these data structures show that our approach can reduce by half the storage space and can improve performance, where the highest performance improvement is achieved for workloads with high update ratios. Our experimental results with the secondary-storage version of the data structures show that our approach produces a solution that can outperform both IBM DB2 and Microsoft SQL Server on the popular TPC-C workload

Estimating the Potential Gains from Mergers: The Danish Agricultural Extension Services

We introduce simple non-parametric models to estimate the potential gains from merging production units. Three effects are distinguished. A merger may affect technical efficiency. It also affects the size of the operation which may or may not be advantageous depending on the return to scale properties of the underlying technologies. Lastly, it affects the mix of inputs available and the mix of outputs demanded. A merged unit face more "balanced" or "harmonic" input and output profiles which is typically advantageous. We use the model to estimate the potential gains from merging agricultural extension offices in Denmark.Teaching/Communication/Extension/Profession,

Estimating the Potential Gains from Mergers

We introduce simple production economic models to estimate the potential gains from mergers. We decompose the gains into technical efficiency, size (scale) and harmony (mix) gains, and we discuss alternative ways to capture these gains. We propose to approximate the production processes using the non-parametric Data Envelopment Analysis (DEA) approach, and we use the resulting operational approach to estimate the potential gains from merging agricultural extension offices in Denmark. Contents: 1. Introduction, 2. Literature, 3. Production Models, 4. Measures of Merger Gains, 5. Decomposing Merger Gains, 6. Alternative Decompositions, 7. The Danish Agricultural Extension Services, 8. Final Remarks, References. Key Words: Data Envelopment Analysis, management, organization, mergersData Envelopment Analysis, management, organization, mergers, Teaching/Communication/Extension/Profession,

Radix Sorting With No Extra Space

It is well known that n integers in the range [1,n^c] can be sorted in O(n) time in the RAM model using radix sorting. More generally, integers in any range [1,U] can be sorted in O(n sqrt{loglog n}) time. However, these algorithms use O(n) words of extra memory. Is this necessary? We present a simple, stable, integer sorting algorithm for words of size O(log n), which works in O(n) time and uses only O(1) words of extra memory on a RAM model. This is the integer sorting case most useful in practice. We extend this result with same bounds to the case when the keys are read-only, which is of theoretical interest. Another interesting question is the case of arbitrary c. Here we present a black-box transformation from any RAM sorting algorithm to a sorting algorithm which uses only O(1) extra space and has the same running time. This settles the complexity of in-place sorting in terms of the complexity of sorting.Comment: Full version of paper accepted to ESA 2007. (17 pages

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes