Event correlation for process discovery from web service interaction logs

Understanding, analyzing, and ultimately improving business processes is a goal of enterprises today. These tasks are challenging as business processes in modern enterprises are implemented over several applications and Web services, and the information about process execution is scattered across several data sources. Understanding modern business processes entails identifying the correlation between events in data sources in the context of business processes (event correlation is the process of finding relationships between events that belong to the same process execution instance). In this paper, we investigate the problem of event correlation for business processes that are realized through the interactions of a set of Web services. We identify various ways in which process-related events could be correlated as well as investigate the problem of discovering event correlation (semi-) automatically from service interaction logs. We introduce the concept of process view to represent the process resulting from a certain way of event correlation and that of process space referring to the set of possible process views over process events. Event correlation is a challenging problem as there are various ways in which process events could be correlated, and in many cases, it is subjective. Exploring all the possibilities of correlations is computationally expensive, and only some of the correlated event sets result in process views that are interesting. We propose efficient algorithms and heuristics to identify correlated event sets that lead potentially to interesting process views. To account for its subjectivity, we have designed the event correlation discovery process to be interactive and enable users to guide it toward process views of their interest and organize the discovered process views into a process map that allows users to effectively navigate through the process space and identify the ones of interest. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.     

Two-dimensional abstract representations (signatures) of proteins

Algorithms have been developed to allow two-dimensional abstract representations of proteins based on: (i) a non-redundant subset of codons, (ii) hydropathy values of amino acids, (iii) the polarities of the amino acid residues and (iv) predicted secondary structures. In addition the suggestion of Gates on nucleic acid representation was implemented. The two-dimensional projections (signatures) that are obtained have the merit that several plots can be represented simultaneously for ready visualization. The approach appears useful in showing relationships among groups of proteins.     

Encoding a process algebra using the Event B method

This paper presents the use of the B technique in its event based definition. We show that it is possible to encode, using Event B, the models (i.e., transition systems) associated to a process algebra with asynchronous semantics. The obtained Event B models consider that the Event B model associated to the left hand side of a BNF rule defining the algebra expressions is refined by a model corresponding to the right hand side of the same rule. The translation rules of each operator of a basic process algebra are given. Then, an example illustrating each translation rule is given. This approach is based on a proof technique and therefore it does not suffer from the state number explosion problem occurring in classical model checking. The interest of this work is the capability to validate user tasks or scenarios when using a given system and particularly a critical system. Finally, we discuss the application of this approach for validating user interfaces tasks in the human–computer interaction area.     

An abstract model of service discovery and binding

We propose a formal operational semantics for service discovery and binding. This semantics is based on a graph-based representation of the configuration of global computers typed by business activities. Business activities execute distributed workflows that can trigger, at run time, the discovery, ranking and selection of services to which they bind, thus reconfiguring the workflows that they execute. Discovery, ranking and selection are based on compliance with required business and interaction protocols and optimisation of quality-of-service constraints. Binding and reconfiguration are captured as algebraic operations on configuration graphs. We also discuss the methodological implications that this model framework has on software engineering using a typical travel-booking scenario. To the best of our knowledge, our approach is the first to provide a clear separation between service computation and discovery/instantiation/binding, and to offer a formal framework that is independent of the SOA middleware components that act as service registries or brokers, and the protocols through which bindings and invocations are performed.     

Behavioral specification of reactive systems using stream-based I/O tables

A core problem in formal methods is the transition from informal requirements to formal specifications. Especially when specifying the behavior of reactive systems, many formalisms require the user to either understand a complex mathematical theory and notation or to derive details not given in the requirements, such as the state space of the problem. For many approaches also a consistent set of requirements is needed, which enforces to resolve requirements conflicts prior to formalization. This paper describes a specification technique, where not states but signal patterns are the main elements. The notation is based on tables of regular expressions and supports a piece-wise formalization of potentially inconsistent requirements. Many properties, such as input completeness and consistency, can be checked automatically for these specifications. The detection and resolution of conflicts can be performed within our framework after formalization. Besides the formal foundation of our approach, this paper presents prototypical tool support and results from an industrial case study.     

Knowledge discovery from process operational data using PCA and fuzzy clustering

An industrial case study is presented which uses principal component analysis and fuzzy c-means clustering to identify operational spaces and develop operational strategies for manufacturing desired products. Analysis of 303 data cases collected from a refinery fluid catalytic cracking process revealed that the data can be projected to four operational zones in the reduced two-dimensional plane. Three zones were found to correspond to three different product grades and the fourth is a zone corresponding to product changeover. Variable contribution analysis was also carried out to identify the most important variables that are responsible for the observed operational spaces and consequently strategies were developed for monitoring and operating the process in order to be able to move the operation from producing one product grade to another, with minimum time delays.     

Learning from abstract and contextualized representations: The effect of verbal guidance

An experiment examined the effects of providing explicit verbal guidance to learners in integrating information with abstract or contextualized representations during computer-based learning of engineering. Verbal guidance supported learners in identifying correspondences and making mental connections among multiple textual and diagrammatic representations. Results from a 2 (abstract (A) or contextualized (C) representation) × 2 (no guidance or guidance) design showed that without guidance, abstract representations led to better transfer than contextualized representations. Moreover, learners in the contextualized representation group benefitted from the guidance, while the abstract representation group did not benefit from guidance. These findings suggest that abstract representations promote the development of deep, transferrable knowledge and that verbal guidance denoting correspondences among representations can facilitate learning when less effective representational formats are utilized.     

Complex phenomena understanding in electricity through dynamically linked concrete and abstract representations

The present study investigates the impact of utilizing virtual laboratory environments combining dynamically linked concrete and abstract representations in investigative activities on the ability of students to comprehend simple and complex phenomena in the field of electric circuits. Forty‐two 16‐ to 17‐year‐old high school students participated in a guided‐inquiry‐based teaching intervention utilizing a virtual laboratory environment and were assigned to three conditions: functional dynamically linked concrete and abstract representations of objects (CA approach), functional concrete representations of objects alone (C approach) and functional abstract representations of objects alone (A approach). All conditions used the same instructor, instructional method and materials. A pretest–post‐test scheme was used to assess the students' conceptual evolution. A repeated measures multivariate analysis of variance of the results indicates that after instruction all groups show a similar significant improvement in comprehending simple phenomena in electric circuits. However, for complex phenomena, the CA approach significantly outperforms the other two. It seems, therefore, that in the field of electric circuits, investigative activities utilizing virtual laboratory environments with dynamically linked concrete and abstract representations of objects may foster enhanced understanding of phenomena with a high degree of complexity for high school students.     

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.     

Image filtering using multiresolution representations

It is shown how multiresolution representations can be used for filter design and implementation. These representations provide a coarse frequency decomposition of the image, which forms the basis for two filtering techniques. The first method, based on image pyramids, is used for approximating the convolution of an image with a given mask. In this technique, a filter is designed using a least-squares procedure based on filters synthesized from the basic pyramid equivalent filters. The second method is an adaptive noise reduction algorithm. An optimally filtered image is synthesized from the multiresolution levels, which in this case are maintained at the original sampling density. Individual pixels of the image representation are linearly combined under a minimum mean square error criterion. This uses a local signal-to-noise ratio estimate to provide the best compromise between noise removal and resolution loss     

基于过程分析的服务发现

在分析若干现有服务匹配算法的基础上,提出了基于过程分析的服务发现算法,利用服务描述中的Process信息,进行更加细化的IO匹配,处理部分Output匹配的情况,提高了服务的查全率。在原型系统的基础上对新的服务发现算法和现有其他算法进行了比较实验,对实验结果的分析证明了新算法的有效性。     

Scalable process discovery and conformance checking

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.     

Encoding process discovery problems in SMT

Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem.     

A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

In this paper, we propose a fuzzy genetic algorithm (Fuzzy-GA) approach integrating fuzzy rule sets and their membership function sets, in a chromosome. The proposed approach consists of two processes: knowledge representation and knowledge assimilation. The knowledge of process parameter setting is encoded as a string with a fuzzy rule set and the associated membership functions. The historical process data forming a combined string is used as the initial knowledge population, which is then ready for knowledge assimilation. A genetic algorithm is used to generate an optimal or nearly optimal fuzzy set and membership functions for the process parameters. The originality of this research is that the proposed system is equipped with the ability to take advantage of assessing the loss which is caused by discrepancy with a process target, thereby enabling the identification of the best set of process parameters. The approach is demonstrated by the use of an experimental example drawn from a semiconductor manufacturer and the results show us that the suggested approach is able to achieve an optimal solution for a process parameter setting problem.     

Learning context-free grammars using tabular representations

We present a novel algorithm using new hypothesis representations for learning context-free grammars from a finite set of positive and negative examples. We propose an efficient hypothesis representation method which consists of a table-like data structure similar to the parse table used in efficient parsing algorithms for context-free grammars such as Cocke-Younger-Kasami algorithm. By employing this representation method, the problem of learning context-free grammars from examples can be reduced to the problem of partitioning the set of nonterminals. We use genetic algorithms for solving this partitioning problem. Further, we incorporate partially structured examples to improve the efficiency of our learning algorithm, where a structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of the unknown grammar. We demonstrate some experimental results using these algorithms and theoretically analyse the completeness of the search space using the tabular method for context-free grammars.     

Time-varying data visualization using functional representations

In many scientific simulations, the temporal variation and analysis of features are important. Visualization and visual analysis of time series data is still a significant challenge because of the large volume of data. Irregular and scattered time series data sets are even more problematic to visualize interactively. Previous work proposed functional representation using basis functions as one solution for interactively visualizing scattered data by harnessing the power of modern PC graphics boards. In this paper, we use the functional representation approach for time-varying data sets and develop an efficient encoding technique utilizing temporal similarity between time steps. Our system utilizes a graduated approach of three methods with increasing time complexity based on the lack of similarity of the evolving data sets. Using this system, we are able to enhance the encoding performance for the time-varying data sets, reduce the data storage by saving only changed or additional basis functions over time, and interactively visualize the time-varying encoding results. Moreover, we present efficient rendering of the functional representations using binary space partitioning tree textures to increase the rendering performance.     

View-based object representations using RBF networks

Radial basis function (RBF) networks have been proposed as suitable representations for 3-D objects, in particular, since they can learn view-based representations from a small set of training views. One of the basic questions that arise in the context of RBF networks concerns their complexity, i.e. the number of basis functions that are necessary for a reliable representation, which should balance the accuracy and the robustness. In this paper, we propose a systematic approach for building object representations in terms of RBF networks. We studied and designed two procedures: the off-line procedure, where the network is constructed after having a complete set of training views of an object, and the on-line procedure, where the network is incrementally built as new views of an object arrive. We tested the procedures both on synthetic and real data.     

Accelerating tableaux proofs using compact representations

In this article a modified form of tableau calculus, calledTableau Graph Calculus, is presented for overcoming the well-known inefficiencies of the traditional tableau calculus to a large extent. This calculus is based on a compact representation of analytic tableaux by using graph structures calledtableau graphs. These graphs are obtained from the input formula in linear time and incorporate most of the rule applications of normal tableau calculus during the conversion process. The size of this representation is linear with respect to the length of the input formula and the graph closely resembles the proof tree of tableau calculi thus retaining the naturalness of the proof procedure. As a result of this preprocessing step, tableau graph calculus has only a single rule which is repeatedly applied to obtain a proof. Many optimizations for the applications of this rule, to effectively prune the search space, are presented as well. Brief details of an implemented prover called FAUST, embedded within the higher-order theorem prover called HOL, are also given. Applications of FAUST within a hardware verification context are also sketched.This work has been partly financed by german national grant under the project Automated System Design, SFB No. 358.