Message type or message cluster extraction is an important task in the analysis of system logs in computer networks. Defining these message types automatically facilitates the automatic analysis of system logs. When the message types that exist in a log file are represented explicitly, they can form the basis for carrying out other automatic application log analysis tasks. In this paper, we introduce a novel algorithm for carrying out message type extraction from event log files. IPLoM, which stands for Iterative Partitioning Log Mining, works through a 4-step process. The first three steps hierarchically partition the event log into groups of event log messages or event clusters. In its fourth and final stage, IPLoM produces a message type description or line format for each of the message clusters. IPLoM is able to find clusters in data irrespective of the frequency of its instances in the data, it scales gracefully in the case of long message type patterns and produces message type descriptions at a level of abstraction, which is preferred by a human observer. Evaluations show that IPLoM outperforms similar algorithms statistically significantly.

A Lightweight Algorithm for Message Type Extraction in System Application Logs

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards “better” processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models “raw data” need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create “flat” event logs that can be analyzed using traditional process-mining techniques.

/pdf/extracting-event-data-from-databases-to-unleash-process-4jnfd8ejvh.pdf

Extracting Event Data from Databases to Unleash Process Mining

Web Services, Service-Oriented Computing, and Service-Oriented Architecture.

A system and method for visualizing objects within an object network. For example, a computer-implemented method according to one embodiment comprises: receiving object graph data from a remote computing system, the object graph data representing characteristics of objects and relationships between objects in object-oriented program code executed on the remote computer system; interpreting the object graph data to determine one or more characteristics of each of the objects; and generating a graphical user interface (“GUI”) comprised of a plurality of graphical nodes arranged in a graph structure, each of the nodes representing one of the objects and the graph structure representing the relationships between the objects, wherein the graphical nodes are rendered with graphical characteristics representing characteristics of the objects which they represent, the graphical characteristics including at least a color and a shape.

Graphical user interface system and method for presenting objects

The increasing code complexity in modern enterprise software systems exceeds the capabilities of most software engineers to understand the system's behavior by just looking at its program code. Large software landscapes, e.g., applications in a cloud infrastructure, further increase this complexity. A solution to these problems is visualizing the applications of the software landscape to ease program comprehension and to understand the respective communication. An established visualization concept is the 3D city metaphor. It utilizes the familiarity with navigating a city to improve program comprehension. Dynamic analysis, e.g., monitoring, can provide the required program traces of the communication. In this paper, we present our live visualization approach of monitoring traces for large software landscapes. It combines a landscape and a system level perspective. The landscape level perspective provides an overview of the software landscape utilizing the viewer's familiarity with UML. The system level perspective provides a visualization utilizing the city metaphor for each software system.

Live trace visualization for comprehending large software landscapes: The ExplorViz approach

The Web Services standard is becoming the lingua franca for loosely coupled distributed applications. As the number of nodes and the complexity of these applications grow over the coming years, it will become more challenging for developers to understand, debug, and optimize them. In this paper, we describe Web Services Navigator, a visualization tool that fosters better understanding of service-oriented architecture (SOA) applications. We draw on our experience with real SOA applications to show how this tool has been applied to practical problems ranging from business logic misunderstandings to performance bottlenecks to syntax and semantic errors. Web Services Navigator helps to solve these problems by visualizing how applications really execute, enabling business owners, application designers, project managers, programmers, and operations staff to understand how their applications actually behave. We sketch the architecture of Web Services Navigator, outline how it reconstructs application execution from event logs, and describe how users interactively explore their applications using its five linked views.

Web services navigator: visualizing the execution of web services

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Web services navigator: visualizing the execution of web services