Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. ex- act clones, can be identified and factored out as shared subprocesses, the repository’s maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

Approximate clone detection in repositories of business process models

Evidence exists that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication may stem from the fact that the repository describes variants of the same pro- cesses and/or because of copy/pasting activity throughout the lifetime of the repository. Previous work has put forward techniques for identifying duplicate fragments (clones) that can be refactored into shared subprocesses. However, these techniques are limited to finding exact clones. This paper analyzes the prob- lem of approximate clone detection and puts forward two techniques for detecting clusters of approximate clones. Experiments show that the proposed techniques are able to accurately retrieve clusters of approximate clones that originate from copy/pasting followed by independent modifications to the copied fragments.

Improving the usability of standard schemas

Due to the development of XML and other data models such as OWL and RDF, sharing data is an increasingly common task since these data models allow simple syntactic translation of data between applications. However, in order for data to be shared semantically, there must be a way to ensure that concepts are the same. One approach is to employ commonly usedschemas—called standard schemas —which help guarantee that syntactically identical objects have semantically similar meanings. As a result of the spread of data sharing, there has been widespread adoption of standard schemas in a broad range of disciplines and for a wide variety of applications within a very short period of time. However, standard schemas are still in their infancy and have not yet matured or been thoroughly evaluated. It is imperative that the data management research community takes a closer look at how well these standard schemas have fared in real-world applications to identify not only their advantages, but also the operational challenges that real users face. In this paper, we both examine the usability of standard schemas in a comparison that spans multiple disciplines, and describe our first step at resolving some of these issues in our Semantic Modeling System. We evaluate our Semantic Modeling System through a careful case study of the use of standard schemas in architecture, engineering, and construction, which we conducted with domain experts. We discuss how our Semantic Modeling System can help the broader problem and also discuss a number of challenges that still remain.

Recipe for success — Lessons learnt from using xAPI within the Connected Learning Analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

A comparative study of multiple attribute tree and inverted file structures for large bibliographic files

A variety of data structures such as inverted file, multi-lists, quad tree, k-d tree, range tree, polygon tree, quintary tree, multidimensional tries, segment tree, doubly chained tree, the grid file, d-fold tree. super B-tree, Multiple Attribute Tree (MAT), etc. have been studied for multidimensional searching and related problems. Physical data base organization, which is an important application of multidimensional searching, is traditionally and mostly handled by employing inverted file. This study proposes MAT data structure for bibliographic file systems, by illustrating the superiority of MAT data structure over inverted file. Both the methods are compared in terms of preprocessing, storage and query costs. Worst-case complexity analysis of both the methods, for a partial match query, is carried out in two cases: (a) when directory resides in main memory, (b) when directory resides in secondary memory. In both cases, MAT data structure is shown to be more efficient than the inverted file method. Arguments are given to illustrate the superiority of MAT data structure in an average case also. An efficient adaptation of MAT data structure, that exploits the special features of MAT structure and bibliographic files, is proposed for bibliographic file systems. In this adaptation, suitable techniques for fixing and ranking of the attributes for MAT data structure are proposed. Conclusions and proposals for future research are presented.

Region Based Structure Layout Optimization by Selective Data Copying

As the gap between processor and memory continues to grow Memory performance becomes a key performance bottleneck for many applications. Compilers therefore increasingly seek to modify an application’s data layout to improve cache locality and cache reuse. Whole program Structure Layout [WPSL] transformations can significantly increase the spatial locality of data and reduce the runtime of programs that use link-based data structures, by increasing the cache line utilization. However, in production compilers WPSL transformations do not realize the entire performance potential possible due to a number of factors. Structure layout decisions made on the basis of whole program aggregated affinity/hotness of structure fields, can be sub optimal for local code regions. WPSL is also restricted in applicability in production compilers for type unsafe languages like C/C++ due to the extensive legality checks and field sensitive pointer analysis required over the entire application. In order to overcome the issues associated with WPSL, we propose Region Based Structure Layout (RBSL) optimization framework, using selective data copying. We describe our RBSL framework, implemented in the production compiler for C/C++ on HP-UX IA-64. We show that acting in complement to the existing and mature WPSL transformation framework in our compiler, RBSL improves application performance in pointer intensive SPEC benchmarks ranging from 3% to 28% over WPSL

Efficient external memory structures for range-aggregate queries

We present external memory data structures for efficiently answering range-aggregate queries. The range-aggregate problem is defined as follows: Given a set of weighted points in R-d, compute the aggregate of the weights of the points that lie inside a d-dimensional orthogonal query rectangle. The aggregates we consider in this paper include COUNT, sum, and MAX. First, we develop a structure for answering two-dimensional range-COUNT queries that uses O(N/B) disk blocks and answers a query in O(log(B) N) I/Os, where N is the number of input points and B is the disk block size. The structure can be extended to obtain a near-linear-size structure for answering range-sum queries using O(log(B) N) I/Os, and a linear-size structure for answering range-MAX queries in O(log(B)(2) N) I/Os. Our structures can be made dynamic and extended to higher dimensions. (C) 2012 Elsevier B.V. All rights reserved.

NOAA/MLML Radiometric Data Acquisition and Processing

This report is a detailed description of data processing of NOAA/MLML spectroradiometry data. It introduces the MLML_DBASE programs, describes the assembly of diverse data fues, and describes general algorithms and how individual routines are used. Definitions of data structures are presented in Appendices. [PDF contains 48 pages]

Data mining the yeast genome in a lazy functional language

Clare, A. and King R.D. (2003) Data mining the yeast genome in a lazy functional language. In Practical Aspects of Declarative Languages (PADL'03) (won Best/Most Practical Paper award).

Quadtree representation and compression of spatial data

Spatial data representation and compression has become a focus issue in computer graphics and image processing applications. Quadtrees, as one of hierarchical data structures, basing on the principle of recursive decomposition of space, always offer a compact and efficient representation of an image. For a given image, the choice of quadtree root node plays an important role in its quadtree representation and final data compression. The goal of this thesis is to present a heuristic algorithm for finding a root node of a region quadtree, which is able to reduce the number of leaf nodes when compared with the standard quadtree decomposition. The empirical results indicate that, this proposed algorithm has quadtree representation and data compression improvement when in comparison with the traditional method.

Big Data or Right Data?

Abstract Big data nowadays is a fashionable topic, independently of what people mean when they use this term. But being big is just a matter of volume, although there is no clear agreement in the size threshold. On the other hand, it is easy to capture large amounts of data using a brute force approach. So the real goal should not be big data but to ask ourselves, for a given problem, what is the right data and how much of it is needed. For some problems this would imply big data, but for the majority of the problems much less data will and is needed. In this talk we explore the trade-offs involved and the main problems that come with big data using the Web as case study: scalability, redundancy, bias, noise, spam, and privacy. Speaker Biography Ricardo Baeza-Yates Ricardo Baeza-Yates is VP of Research for Yahoo Labs leading teams in United States, Europe and Latin America since 2006 and based in Sunnyvale, California, since August 2014. During this time he has lead the labs in Barcelona and Santiago de Chile. Between 2008 and 2012 he also oversaw the Haifa lab. He is also part time Professor at the Dept. of Information and Communication Technologies of the Universitat Pompeu Fabra, in Barcelona, Spain. During 2005 he was an ICREA research professor at the same university. Until 2004 he was Professor and before founder and Director of the Center for Web Research at the Dept. of Computing Science of the University of Chile (in leave of absence until today). He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electronics engineer degree from the University of Chile in Santiago. He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, that won the ASIST 2012 Book of the Year award. He is also co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 500 other publications. From 2002 to 2004 he was elected to the board of governors of the IEEE Computer Society and in 2012 he was elected for the ACM Council. He has received the Organization of American States award for young researchers in exact sciences (1993), the Graham Medal for innovation in computing given by the University of Waterloo to distinguished ex-alumni (2007), the CLEI Latin American distinction for contributions to CS in the region (2009), and the National Award of the Chilean Association of Engineers (2010), among other distinctions. In 2003 he was the first computer scientist to be elected to the Chilean Academy of Sciences and since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow.

Modeling data processing activity to assess the internal control function

This thesis describes research that was conducted into the potential of modeling the activities of the Data Processing Department as an aid to the computer auditor. A methodology is composed to aid in the evaluation of the Internal Controls, particularly the General Controls relative to computer processing. Consisting of three major components, the methodology enables the auditor to model the presumed activities of the Data Processing Department against the actual activities, as recorded on the Operating System Log. The first component of the methodology is the construction and loading of a model of the presumed activities of the Data Processing Department from its verbal, scheduled, and reported activities. The second component is the generation of a description of the actual activities of the Data Processing Department from the information recorded on the Operating System Log. This is effected by reducing the Operating System Log to the format described by the Standard Audit File concept. Finally, the third component in the methodology is the modeling process itself. This is in fact a new analysis technique proposed for use by the EDP auditor. The modeling process is composed of software that compares the model developed and loaded in the first component, with the description of actual activity as collated by the second component. Results from this comparison are then reviewed by the auditor, who determines if they adequately depict the situation, or whether the models description as specified in the first component requires to be altered, and the modeling process re-initiated. In conducting the research, information and data from a production installation was used. Use of the ‘real-world’ input proved both the feasibility of developing a model of the reported activities of the Data Processing Department, and the adequacy of the operating system log as a source of information to report the departments actual activities. Additionally, it enabled the involvement and comment of practicing auditors. The research involved analysis of the effect of EDP on the audit process, structure of the EDP audit process, data reduction, data structures, model formalization, and model processing software. Additionally, the Standard Audit File concept was verified through its use by practising auditors, and expanded by the development of an indexed data structure, which enabled its analysis to be conducted interactively. Results from the trial implementation of the research software and methodology at a production installation confirmed the research hypothesis that the activities of the Data Processing Department could be modelled, and that there are substantial benefits from the EDP auditor in analysing this process. The research in fact provides a new source of information, and develops a new analysis technique for the EDP auditor. It demonstrates the utilization of computer technology to monitor itself for the audit function, and reasserts auditor independence by providing access to technical detail describing the processing activities of the computer.

Infrequent item mining in multiple data streams

The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques. © 2007 Crown Copyright.