Deriving artefact-centric interfaces for overloaded web services

We present a novel framework and algorithms for the analysis of Web service interfaces to improve the efficiency of application integration in wide-spanning business networks. Our approach addresses the notorious issue of large and overloaded operational signatures, which are becoming increasingly prevalent on the Internet and being opened up for third-party service aggregation. Extending upon existing techniques used to refactor service interfaces based on derived artefacts of applications, namely business entities, we propose heuristics for deriving relations between business entities, and in turn, deriving permissible orders in which operations are invoked. As a result, service operations are refactored on business entity CRUD which then leads to behavioural protocols generated, thus supportive of fine-grained and flexible service discovery, composition and interaction. A prototypical implementation and analysis of web services, including those of commercial logistic systems (Fedex), are used to validate the algorithms and open up further insights into service interface synthesis.

Discovering behavioural interfaces for overloaded web services

The growth of APIs and Web services on the Internet, especially through larger enterprise systems increasingly being leveraged for Cloud and software-as-a-service opportuni- ties, poses challenges to improving the efficiency of integration with these services. Interfaces of enterprise systems are typically larger, more complex and overloaded, with single operation having multiple data entities and parameter sets, supporting varying requests, and reflecting versioning across different system releases, compared to fine-grained operations of contemporary interfaces. We propose a technique to support the refactoring of service interfaces by deriving business entities and their relationships. In this paper, we focus on the behavioural aspects of service interfaces, aiming to discover the sequential dependencies of operations (otherwise known as protocol extraction) based on the entities and relationships derived. Specifically, we propose heuristics according to these relationships, and in turn, deriving permissible orders in which operations are invoked. As a result of this, service operations can be refactored on business entity CRUD lines, with explicit behavioural protocols as part of an interface definition. This supports flexible service discovery, composition and integration. A prototypical implementation and analysis of existing Web services, including those of commercial logistic systems (Fedex), are used to validate the algorithms proposed through the paper.

The evolution of entrepreneurial learning

- Purpose The purpose of this paper is to present an evolutionary perspective on entrepreneurial learning, whilst also accounting for fundamental ecological processes, by focusing on the development of key recurring, knowledge components within nascent and growing small businesses. - Design/methodology/approach The paper relates key developments within the organizational evolution literature to research on entrepreneurial learning, with arguments presented in favor of adopting a multi‐level co‐evolutionary perspective that captures and explains hidden ecological process, such as niche‐construction. - Findings It is argued in the paper that such a multi‐level focus on key recurring knowledge components can shed new light on the process of entrepreneurial learning and lead to the cross‐fertilization of ideas across different domains of study, by offering researchers the opportunity to use the framework of variation‐selection‐retention to develop a multi‐level representation of organizational and entrepreneurial learning. - Originality/value Entrepreneurial learning viewed in this way, as a multi‐level struggle for survival amongst competing knowledge components, can provide entrepreneurs with a set of evolutionary heuristics as they re‐interpret their understanding of the evolution of their business.

Developing an evolutionary/ecological approach in enterprise education

This paper presents an ecological/evolutionary approach to enterprise education. Ecological approaches are used at the University of Tasmania to heighten the awareness of students to a raft of difficult to observe environmental factors associated with developing enterprising ideas. At Sheffield University, the discovery and exploitation of entrepreneurial opportunities is viewed as a co-evolving system of emerging business ideas, and routines/heuristics respectively. It is argued that using both approaches enables students to develop a greater awareness of their situated environment, and ultimately the degree of fit between their learning process and a changing external world. The authors argue that in order to improve the chances of longer-term survival what is needed is a new level of organisation where the individual is capable of developing a representation of the external world that he or she can use to sense the appropriateness of local decisions. This reinterpretation of events allows individuals to step back and examine the broader consequences of their actions through the interpretation and anticipation of feedback from the environment. These approaches thus seek to develop practice-based heuristics which individuals can use to make sense of their lived experiences, as they learn to evolve in an increasingly complex world.

Construction of a global map of human gene expression : the process, tools and analysis

This thesis studies human gene expression space using high throughput gene expression data from DNA microarrays. In molecular biology, high throughput techniques allow numerical measurements of expression of tens of thousands of genes simultaneously. In a single study, this data is traditionally obtained from a limited number of sample types with a small number of replicates. For organism-wide analysis, this data has been largely unavailable and the global structure of human transcriptome has remained unknown. This thesis introduces a human transcriptome map of different biological entities and analysis of its general structure. The map is constructed from gene expression data from the two largest public microarray data repositories, GEO and ArrayExpress. The creation of this map contributed to the development of ArrayExpress by identifying and retrofitting the previously unusable and missing data and by improving the access to its data. It also contributed to creation of several new tools for microarray data manipulation and establishment of data exchange between GEO and ArrayExpress. The data integration for the global map required creation of a new large ontology of human cell types, disease states, organism parts and cell lines. The ontology was used in a new text mining and decision tree based method for automatic conversion of human readable free text microarray data annotations into categorised format. The data comparability and minimisation of the systematic measurement errors that are characteristic to each lab- oratory in this large cross-laboratories integrated dataset, was ensured by computation of a range of microarray data quality metrics and exclusion of incomparable data. The structure of a global map of human gene expression was then explored by principal component analysis and hierarchical clustering using heuristics and help from another purpose built sample ontology. A preface and motivation to the construction and analysis of a global map of human gene expression is given by analysis of two microarray datasets of human malignant melanoma. The analysis of these sets incorporate indirect comparison of statistical methods for finding differentially expressed genes and point to the need to study gene expression on a global level.

Matrix Decomposition Methods for Data Mining : Computational Complexity and Algorithms

Matrix decompositions, where a given matrix is represented as a product of two other matrices, are regularly used in data mining. Most matrix decompositions have their roots in linear algebra, but the needs of data mining are not always those of linear algebra. In data mining one needs to have results that are interpretable -- and what is considered interpretable in data mining can be very different to what is considered interpretable in linear algebra. --- The purpose of this thesis is to study matrix decompositions that directly address the issue of interpretability. An example is a decomposition of binary matrices where the factor matrices are assumed to be binary and the matrix multiplication is Boolean. The restriction to binary factor matrices increases interpretability -- factor matrices are of the same type as the original matrix -- and allows the use of Boolean matrix multiplication, which is often more intuitive than normal matrix multiplication with binary matrices. Also several other decomposition methods are described, and the computational complexity of computing them is studied together with the hardness of approximating the related optimization problems. Based on these studies, algorithms for constructing the decompositions are proposed. Constructing the decompositions turns out to be computationally hard, and the proposed algorithms are mostly based on various heuristics. Nevertheless, the algorithms are shown to be capable of finding good results in empirical experiments conducted with both synthetic and real-world data.

Minimization of Incompletely Specified Sequential Machines

A simple yet efficient method for the minimization of incompletely specified sequential machines (ISSMs) is proposed. Precise theorems are developed, as a consequence of which several compatibles can be deleted from consideration at the very first stage in the search for a minimal closed cover. Thus, the computational work is significantly reduced. Initial cardinality of the minimal closed cover is further reduced by a consideration of the maximal compatibles (MC's) only; as a result the method converges to the solution faster than the existing procedures. "Rank" of a compatible is defined. It is shown that ordering the compatibles, in accordance with their rank, reduces the number of comparisons to be made in the search for exclusion of compatibles. The new method is simple, systematic, and programmable. It does not involve any heuristics or intuitive procedures. For small- and medium-sized machines, it canle used for hand computation as well. For one of the illustrative examples used in this paper, 30 out of 40 compatibles can be ignored in accordance with the proposed rules and the remaining 10 compatibles only need be considered for obtaining a minimal solution.

Valence and spatial explanations for voting in the 2013 Australian election

This paper examines the 2013 Australian federal election to test two competing models of vote choice: spatial politics and valence issues. Using data from the 2013 Australian Election Study, the analysis finds that spatial politics (measured by party identification and self-placement on the left-right spectrum) and valence issues both have significant effects on vote choice. However, spatial measures are more important than valence issues in explaining vote choice, in contrast with recent studies from Britain, Canada and the United States. Explanations for these differences are speculative, but may relate to Australia’s stable party and electoral system, including compulsory voting and the frequency of elections. The consequently high information burden faced by Australian voters may lead to a greater reliance on spatial heuristics than is found elsewhere.

An operational-level multi-stage mine production timetabling model for optimally synchronising drilling, blasting and excavating operations

This paper proposes a new multi-stage mine production timetabling (MMPT) model to optimise open-pit mine production operations including drilling, blasting and excavating under real-time mining constraints. The MMPT problem is formulated as a mixed integer programming model and can be optimally solved for small-size MMPT instances by IBM ILOG-CPLEX. Due to NP-hardness, an improved shifting-bottleneck-procedure algorithm based on the extended disjunctive graph is developed to solve large-size MMPT instances in an effective and efficient way. Extensive computational experiments are presented to validate the proposed algorithm that is able to efficiently obtain the near-optimal operational timetable of mining equipment units. The advantages are indicated by sensitivity analysis under various real-life scenarios. The proposed MMPT methodology is promising to be implemented as a tool for mining industry because it is straightforwardly modelled as a standard scheduling model, efficiently solved by the heuristic algorithm, and flexibly expanded by adopting additional industrial constraints.

Design of multiunit electronic exchanges through decomposition

In this paper, we exploit the idea of decomposition to match buyers and sellers in an electronic exchange for trading large volumes of homogeneous goods, where the buyers and sellers specify marginal-decreasing piecewise constant price curves to capture volume discounts. Such exchanges are relevant for automated trading in many e-business applications. The problem of determining winners and Vickrey prices in such exchanges is known to have a worst-case complexity equal to that of as many as (1 + m + n) NP-hard problems, where m is the number of buyers and n is the number of sellers. Our method proposes the overall exchange problem to be solved as two separate and simpler problems: 1) forward auction and 2) reverse auction, which turns out to be generalized knapsack problems. In the proposed approach, we first determine the quantity of units to be traded between the sellers and the buyers using fast heuristics developed by us. Next, we solve a forward auction and a reverse auction using fully polynomial time approximation schemes available in the literature. The proposed approach has worst-case polynomial time complexity. and our experimentation shows that the approach produces good quality solutions to the problem. Note to Practitioners- In recent times, electronic marketplaces have provided an efficient way for businesses and consumers to trade goods and services. The use of innovative mechanisms and algorithms has made it possible to improve the efficiency of electronic marketplaces by enabling optimization of revenues for the marketplace and of utilities for the buyers and sellers. In this paper, we look at single-item, multiunit electronic exchanges. These are electronic marketplaces where buyers submit bids and sellers ask for multiple units of a single item. We allow buyers and sellers to specify volume discounts using suitable functions. Such exchanges are relevant for high-volume business-to-business trading of standard products, such as silicon wafers, very large-scale integrated chips, desktops, telecommunications equipment, commoditized goods, etc. The problem of determining winners and prices in such exchanges is known to involve solving many NP-hard problems. Our paper exploits the familiar idea of decomposition, uses certain algorithms from the literature, and develops two fast heuristics to solve the problem in a near optimal way in worst-case polynomial time.

Novel melody line identification algorithm for polyphonic MIDI music

The problem of automatic melody line identification in a MIDI file plays an important role towards taking QBH systems to the next level. We present here, a novel algorithm to identify the melody line in a polyphonic MIDI file. A note pruning and track/channel ranking method is used to identify the melody line. We use results from musicology to derive certain simple heuristics for the note pruning stage. This helps in the robustness of the algorithm, by way of discarding "spurious" notes. A ranking based on the melodic information in each track/channel enables us to choose the melody line accurately. Our algorithm makes no assumption about MIDI performer specific parameters, is simple and achieves an accuracy of 97% in identifying the melody line correctly. This algorithm is currently being used by us in a QBH system built in our lab.

Microarchitecture sensitive empirical models for compiler optimizations.

This paper proposes the use of empirical modeling techniques for building microarchitecture sensitive models for compiler optimizations. The models we build relate program performance to settings of compiler optimization flags, associated heuristics and key microarchitectural parameters. Unlike traditional analytical modeling methods, this relationship is learned entirely from data obtained by measuring performance at a small number of carefully selected compiler/microarchitecture configurations. We evaluate three different learning techniques in this context viz. linear regression, adaptive regression splines and radial basis function networks. We use the generated models to a) predict program performance at arbitrary compiler/microarchitecture configurations, b) quantify the significance of complex interactions between optimizations and the microarchitecture, and c) efficiently search for'optimal' settings of optimization flags and heuristics for any given microarchitectural configuration. Our evaluation using benchmarks from the SPEC CPU2000 suits suggests that accurate models (< 5% average error in prediction) can be generated using a reasonable number of simulations. We also find that using compiler settings prescribed by a model-based search can improve program performance by as much as 19% (with an average of 9.5%) over highly optimized binaries.

Profitable Customer Management: A Study in Retail Banking

The increasing focus of relationship marketing and customer relationship management (CRM) studies on issues of customer profitability has led to the emergence of an area of research on profitable customer management. Nevertheless, there is a notable lack of empirical research examining the current practices of firms specifically with regard to the profitable management of customer relationships according to the approaches suggested in theory. This thesis fills this research gap by exploring profitable customer management in the retail banking sector. Several topics are covered, including marketing metrics and accountability; challenges in the implementation of profitable customer management approaches in practice; analytic versus heuristic (‘rule of thumb’) decision making; and the modification of costly customer behavior in order to increase customer profitability, customer lifetime value (CLV), and customer equity, i.e. the financial value of the customer base. The thesis critically reviews the concept of customer equity and proposes a Customer Equity Scorecard, providing a starting point for a constructive dialog between marketing and finance concerning the development of appropriate metrics to measure marketing outcomes. Since customer management and measurement issues go hand in hand, profitable customer management is contingent on both marketing management skills and financial measurement skills. A clear gap between marketing theory and practice regarding profitable customer management is also identified. The findings show that key customer management aspects that have been proposed within the literature on profitable customer management for many years, are not being actively applied by the banks included in the research. Instead, several areas of customer management decision making are found to be influenced by heuristics. This dilemma for marketing accountability is addressed by emphasizing that CLV and customer equity, which are aggregate metrics, only provide certain indications regarding the relative value of customers and the approximate value of the customer base (or groups of customers), respectively. The value created by marketing manifests itself in the effect of marketing actions on customer perceptions, behavior, and ultimately the components of CLV, namely revenues, costs, risk, and retention, as well as additional components of customer equity, such as customer acquisition. The thesis also points out that although costs are a crucial component of CLV, they have largely been neglected in prior CRM research. Cost-cutting has often been viewed negatively in customer-focused marketing literature on service quality and customer profitability, but the case studies in this thesis demonstrate that reduced costs do not necessarily have to lead to lower service quality, customer retention, and customer-related revenues. Consequently, this thesis provides an expanded foundation upon which marketers can stake their claim for accountability. By focusing on the range of drivers and all of the components of CLV and customer equity, marketing has the potential to provide specific evidence concerning how various activities have affected the drivers and components of CLV within different groups of customers, and the implications for customer equity on a customer base level.

Luotettavaa tietoa ja merkityksellisiä valintoja – Kuluttajien ympäristövastuullista järkeilyä ilmastonmuutoksesta ja ohjausjärjestelmien hyväksyttävyydestä

Tutkielmassa tarkastellaan kuluttajien näkemyksiä ilmastonmuutoksesta ja ilmastovaikutusten seuranta- ja palautejärjestelmän hyväksyttävyydestä kulutuksen ohjauskeinona. 15 kuluttajaa kokeili kuukauden ajan kulutuksen ilmastovaikutusten seuranta- ja palautejärjestelmän demonstraatioversiota ja he osallistuivat kokeilun pohjalta aihepiiriä käsitelleeseen verkkokeskusteluun. Analysoin verkkokeskustelun aineistoa arkisen järkeilyn näkökulmasta tutkien kuluttajien ilmastonmuutokseen ja ympäristövastuullisuuteen liittyvää arkitietoa sekä palvelun hyväksyttävyyteen liittyviä heuristiikkoja. Ilmastonmuutoksen todettiin yleisesti olevan vielä melko abstrakti ja monitulkintainen ilmiö, minkä vuoksi kuluttajilla on vaikeuksia ymmärtää omien valintojensa konkreettista merkitystä ilmastonmuutoksen kannalta. Vaikka tietoa kulutuksen ilmastovaikutuksista on saatavilla paljon, niin erityisesti yritysten tuottama tieto koettiin ristiriitaiseksi ja osin epäluotettavaksi. Kuluttajat myös kritisoivat ilmastonmuutoskeskustelun tarjoamaa kapeaa näkemystä kulutuksen ympäristövaikutuksista. Hiilidioksidipäästöihin keskittymisen sijaan ympäristövaikutuksia tulisi kuluttajien mielestä tarkastella kokonaisuutena, josta ilmastovaikutukset muodostavat vain yhden osan. Tutkimukseen osallistuneiden kuluttajien kulutustottumuksiin ilmastonmuutos vaikutti eriasteisesti. Toisille ilmastonmuutoksesta oli muodostunut keskeinen omaa kulutusta ohjaava normi, kun taas toiset kertoivat pohtivansa ilmastovaikutuksia pääasiassa suurimpien hankintojen kohdalla. Ympäristövastuullisuudessa merkitykselliseksi koettiin tasapainon löytäminen ja henkilökohtainen tunne siitä, että kokee toimivansa oikein. Ilmasto- tai ympäristökysymyksiä punnitaan valinnoissa joustavasti yhdessä muiden tekijöiden kanssa. Vaikka kuluttajilla toisaalta olisi tietoa ja halua ottaa ilmasto- ja ympäristövaikutukset huomioon valinnoissaan, toimintaympäristö rajaa keskeisesti kuluttajien mahdollisuuksia toimia ympäristövastuullisesti. Tutkimus toi esille neljä kuluttajien käyttämää heuristiikkaa heidän pohtiessaan ilmastovaikutusten seuranta- ja palautejärjestelmän hyväksyttävyyden ehtoja ja toimivuutta ohjauskeinona. Ensinnäkin palvelun tulee olla käytettävyydeltään nopea ja vaivaton sekä tarjota tietoa havainnollisessa ja helposti ymmärrettävässä muodossa. Toiseksi palvelun tarjoaman tiedon tulee olla ehdottoman luotettavaa ja kuluttajien valintojen kannalta merkityksellistä siten, että palvelu ottaa huomioon erilaiset kuluttajat ja tiedontarpeet. Kolmanneksi palvelu tulee toteuttaa kokonaisvaltaisesti ja läpinäkyvästi useampien kaupparyhmittymien ja julkisten toimijoiden yhteistyönä. Neljänneksi toteutuksessa tulee huomioda palvelun kannustavuus ja kytkeytyminen muihin ohjauskeinoihin.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.