Transition Systems Reduction: Balancing between Precision and Simplicity
Human reasoning uses to distinguish things that do change and things do not. The latter are commonly expressed in the reasoning as objects, which may represent classes or instances, and classes being further divided into concept types and relation types. These became the main issue of knowledge engineering and have been well tractable by computer. The former kind of things, meanwhile, inevitably evokes consideration not only of a ``thing-that-changes'' but also of ``change-of-a-thing'' and thus claims that the change itself be another entity that needs to be comprehended and handled. This special entity, being treated from different perspectives as event, (changeable) state, transformation, process, scenario and the like, remains a controversial philosophical, linguistic and scientific entity and has gained notably less systematic attention by knowledge engineers than non-changing things. In particular, there is no clarity in how to express the change in knowledge engineering -– as some specific concept or relation type, as a statement, or proposition, in which subject is related to predicate(s), or in another way. There seems to be an agreement among the scientists that time has to be related, explicitly or implicitly, to everything we regard as change -– but the way it should be related, and whether this should be exactly the time or some generic property or condition, is also an issue of debate. To bring together the researchers who study representation of change in knowledge engineering both in fundamental and applied aspects, a workshop on Modeling States, Events, Processes and Scenarios (MSEPS 2013) was run on 12 January, 2013, in the framework of the 20th International Conference on Conceptual Structures (ICCS 2013) in Mumbai, India. Seven submissions were selected for presentation that cover major approaches to representation of the change and address such diverse domains of knowledge as biology, geology, oceanography, physics, chemistry and also some multidisciplinary contexts. Concept maps of biological and other transformations were presented by Meena Kharatmal and Nagarjuna Gadiradju. Their approach stems from conceptual graphs of Sowa and represents the vision of change as a particular type of concept or, likely, relation, defined by meaning rather than by formal properties. The work of Prima Gustiene and Remigijus Gustas follows a congenial approach but develops a different notation for representation of the change based on specified actor dependencies in application to business issues concerning privacy-related data. Nataly Zhukova, Oksana Smirnova and Dmitry Ignatov explore the structure of oceanographic data in concern of opportunity of their representation by event ontologies and conceptual graphs. Vladimir Anokhin and Biju Longhinos examine another Earth science, geotectonics, and demonstrate that its long-lasting methodological problems urge application of knowledge engineering methods, primarily engineering of knowledge about events and processes. They suggest a draft of application strategy of knowledge engineering in geotectonics and claim for a joint interdisciplinary effort in this direction. Doji Lokku and Anuradha Alladi introduce a concept of ``purposefulness'' for any human action and suggest a modeling approach based on it in the systems theory context. In this approach, intellectual means for reaching a purpose are regarded either as structure of a system, in which the purpose is achieved, or as a process that takes place in this system. These means are exposed to different concerns of knowledge, which may be either favorable or not to achieving the purpose. The resulting framework perhaps can be described in a conceptual-graph-related way but is also obviously interpretable as a statement-based pattern, more or less resembling the event bush (Pshenichny et al., 2009). This binds all the aforementioned works with the last two contributions, which represent an approach based on understanding of the change as a succession of events (including at least one event), the latter being expressed as a statement with one subject and finite number of predicates. The method of event bush that materializes this approach, previously applied mostly in the geosciences, is demonstrated here in application to physical modeling by Cyril Pshenichny, Roberto Carniel and Paolo Diviacco and to chemical and experimental issues, by Cyril Pshenichny. The reported results and their discussion form an agenda for future meetings, discussions and publications. This agenda includes, though is not limited to, - logical tools for processes modeling, - visual notations for dynamic knowledge representation, - graph languages and graph semantics, - semantic science applications, - event-driven reasoning, - ontological modeling of events and time, - process mining, - modeling of events, states, processes and scenarios in particular domains and interdisciplinary contexts. The workshop has marked the formation of a new sub-discipline in the knowledge engineering, and future effort will be directed to consolidate its conceptual base and transform the existing diversity of approaches to representation of the change into an arsenal of complementary tools sharpened for various spectral regions of tasks in different domains.
BPM 2013 was the 11th conference in a series that provides a prestigious forum for researchers and practitioners in the field of business process management (BPM). The conference was organized by Tsinghua University, China, and took place during August 26–30, 2013, in Beijing, China. Compared to previous editions of BPM, this year we noted a lower focus by authors on topics like process modeling, while we also observed a considerable growth of submissions regarding areas like process mining, conformance/compliance checking, and process model matching. The integrated consideration of processes and data remains popular, and novel viewpoints focus, among others, on data completeness in business processes, the modeling and runtime support of event streaming in business processes, and business process architectures.
Журналы событий, сохраняемые современными информационными и техническими системами, как правило, содержат достаточно данных для автоматизированного восстановления моделей соответствующих процессов. Разработано множество алгоритмов для построения моделей процессов, проверки соответствия фактического поведения системы модельному, сравнения моделей процессов, и т.д. Однако возможность быстрого анализа выбираемых пользователями частей журнала до сих пор не нашла полноценной реализации. В статье описан метод многомерного хранения журналов событий для извлечения и анализа процессов, основанный на подходе ROLAP. Результатом анализа журнала является направленный невзвешенный граф, представляющий собою сумму возможных последовательностей событий, упорядоченных по вероятности их возникновения с учетом заданных условий. Разработанный инструмент позволяет выполнять совместный анализ моделей подпроцессов, восстановленных из частей журнала путем задания критериев отбора событий и требуемого уровня детализации модели.
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.
This volume contains the extended version of selected talks given at the international research workshop "Coping with Complexity: Model Reduction and Data Analysis", Ambleside, UK, August 31 – September 4, 2009. The book is deliberately broad in scope and aims at promoting new ideas and methodological perspectives. The topics of the chapters range from theoretical analysis of complex and multiscale mathematical models to applications in e.g., fluid dynamics and chemical kinetics.
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.