Inductive benchmarking for purely functional data structures

Every designer of a new data structure wants to know how well it performs in comparison with others. But finding, coding and testing applications as benchmarks can be tedious and time-consuming. Besides, how a benchmark uses a data structure may considerably affect its apparent efficiency, so the choice of applications may bias the results. We address these problems by developing a tool for inductive benchmarking. This tool, Auburn, can generate benchmarks across a wide distribution of uses. We precisely define 'the use of a data structure', upon which we build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application. We then apply inductive classification techniques to obtain decision trees for the choice between competing data structures. We test Auburn by benchmarking several implementations of three common data structures: queues, random-access lists and heaps. These and other results show Auburn to be a useful and accurate tool, but they also reveal some limitations of the approach

Benchmarking purely functional data structures.

When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves finding, coding and testing some applications to act as benchmarks. This can be tedious and time-consuming. Worse, how a benchmark uses a data structure may considerably affect the efficiency of the data structure. Thus, the choice of benchmarks may bias the results. For these reasons, new data structures developed for functional languages often pay little attention to empirical performance. We solve these problems by developing a benchmarking tool, Auburn, that can generate benchmarks across a fair distribution of uses. We precisely define "the use of a data structure", upon which we build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application. We consider how best to use these algorithms to benchmark competing data structures. Finally, we test Auburn by benchmarking ..

06472 Abstracts Collection - XQuery Implementation Paradigms

From 19.11.2006 to 22.11.2006, the Dagstuhl Seminar 06472 ``XQuery Implementation Paradigms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Benchmarking the business performance of departmental space in universities

Purpose and Theory: In UK higher education institutions, facilities management performance tends to be measured in space utilisation and space cost. A new approach uses the �return on investment� (ROI) concept of income generation to highlight space performance at faculty/department/building level. Design and approach: Using space data from several English universities and data envelopment analysis (DEA), six types of academic units (departments, institutes or similar) are compared in regard of their respective research and teaching income. This technique allows mapping out the total �envelope� with the best performers at the edge, showing what improvement/change would be needed for the others in the group to match their performance. Findings: This is a viable method of benchmarking and gives participating institutions better and more strategic and business-oriented feedback on the performance of their space envelope than mere cost comparisons. It can potentially inform strategic decisions about university estates. However, there are barriers to applying this approach: problems posed by issues of classification and diverse organisational structures can be overcome, but lack of collaboration of facilities/estates and finance directorates; lack of centralised, accurate and detailed data pose more serious challenges

Configurations Driving NPD Performance Fit with Market Demands and Time Constraints

The research reported in this paper is aimed at developing knowledge on organizing NPD systems to optimize their contribution to performance. To this end, a systems approach to fit is used to explain the context-structure-performance relationships for NPD performance, specifically in terms of fit with market demands of the product concept and fit with time constraints of the development process. From a sample of 164 US firms, the top 15 % performers in terms of both fit with market demands and fit with time constraints have been identified. An optimized ‘Ideal Profile’ for the organization of NPD systems, formed by a consistent pattern of: NPD Process, NPD Project Structure and Management, Innovation Climate, and NPD Goal Setting and Portfolio Management, followed from the analysis of the NPD configuration of these top performers. For the calibration sample (the other 85%) significant deviation from the ideal profile on all elements of the configuration was found, the correlations between NPD Performance Fit with Market Demands and Fit with Time Constraints and total Euclidean distance are also significant. Overall, these results provide evidence for the proposition that (1) new product success is a function of a set of NPD development system decisions and (2) to truly understand the impact of those decisions, they must be considered as a holistic system.\ud The contribution of this research is in the empirical validation of the internal consistency of an ideal organizational profile for NPD systems achieving both a high NPD performance in terms of market acceptance of their new products as well in terms of the satisfactory level of the development times of those products. By also examining ideal profiles for each of these NPD performance dimensions separately, the conflicting demands created by multiple performance metrics are highlighted as well as the organizational trade-offs necessary for optimal performance. In terms of managerial implications, this also gives direction for organizational redesign to firms either wanting to maximize their product concept (Fit with Market Demands) or development process (Fit with Time Constraints) performance