Data Mining Practical Machine Learning Tools and Techniques

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

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.

/pdf/scalable-process-discovery-and-conformance-checking-2a5tlc8rw0.pdf

Scalable process discovery and conformance checking

http://bpmcenter.org/wp-content/uploads/reports/2012/BPM-12-01.pdf

Editorial: Managing large collections of business process models-Current techniques and challenges

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log. In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions. This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.

Quality dimensions in process discovery : the importance of fitness, precision, generalization and simplicity

With the increasing influence of Business Process Management, large process model repositories emerged in enterprises and public administrations. Their effective utilization requires meaningful and efficient capabilities to search for models that go beyond text based search or folder navigation, e.g., by similarity. Existing measures for process model similarity are often not applicable for efficient similarity search, as they lack metric features. In this paper, we introduce a proper metric to quantify process similarity based on behavioral profiles. It is grounded in the Jaccard coefficient and leverages behavioral relations between pairs of process model activities. The metric is successfully evaluated towards its approximation of human similarity assessment.

https://matthiasweidlich.com/paper/similarity_metric_BPM_2011.pdf

Behavioral similarity: a proper metric

Due to the increasing adoption of business process management and the key role of process models, companies are setting up and maintaining large process model repositories. Repositories containing hundreds or thousands of process models are not uncommon, whereas only simplistic search functionality, such as text based search or folder navigation, is provided, today.

Metric Trees for Efficient Similarity Search in Large Process Model Repositories

Business process models are typically graphs that communicate knowledge about the work performed in organizations. Collections of these models are gathered to analyze and improve the way an organization operates. From a research perspective, these collections tell about modeling styles, the relevance of modeling constructs, and common formal modeling mistakes.

https://www.matthiasweidlich.com/paper/understanding_modelling_BPM_AI_BPMN_2011.pdf

Towards Understanding Process Modeling – The Case of the BPM Academic Initiative

During business process design, working procedures in organizations are represented by process models. It is an important task in any process improvement project, yet time consuming and error prone. While many organizations maintain large process model repositories, we observe that the information these repositories carry is not fully exploited during process modeling. In this paper, we present a novel approach to business process design called Design by Selection, which takes advantage of process repositories during design and facilitates reuse of process model components. These components can be static or flexible. Static ones represent the specific aspects of the process model, while flexible components realize re-use: They are defined by visual queries, which return matching process model components to be embedded in the overall process. Thus, process models can be designed in a more efficient, higher quality, and less error-prone way.

http://er2011.ulb.ac.be/media/awad_er2011.pdf

Design by selection: a reuse-based approach for business process modeling

Process model similarity has developed into a prolific field of investigation. This paper summarizes the research after the CAISE 2008 paper on this topic. We identify categories of problems and provide an outlook on future directions.

/pdf/a-short-survey-on-process-model-similarity-1hwzw0qnjt.pdf

Matthias Kunze

Papers

Behavioral similarity: a proper metric

Metric Trees for Efficient Similarity Search in Large Process Model Repositories

Towards Understanding Process Modeling – The Case of the BPM Academic Initiative

Design by selection: a reuse-based approach for business process modeling

A Short Survey on Process Model Similarity