Development of the representation of cause-effect dependencies for the knowledge base of the business process management system

Досліджено проблему побудови представлення знань в системі процесного управління для знання-ємних бізнес-процесів в аспекті відображення причинно-наслідкових зв’язків між контекстом виконання дій та діями бізнес-процесу. Показано, що загальний підхід до вирішення цієї проблеми пов’язаний із виділенням каузальних залежностей на основі аналізу логу подій, що містить записи про поведінку бізнес-процесу. При вирішенні проблеми сформульовано задачі уточнення структури представлення причинно-наслідкового аспекту знань у відповідності до особливостей логу; побудови логічних фактів і правил у відповідності до структури подій логу; формалізації представлення знань з урахуванням фактів, правил та апріорних обмежень. Встановлено, що зв’язок між артефактами контексту та подіями логу бізнес-процесу здійснюється через спільні атрибути. Між артефактами й атрибутами та між подіями й атрибутами існує відношення один до багатьох. Структурована логічна складову бази знань у вигляді логічних фактів та правил. Логічні факти визначають стан бізнес-процесу у дискретні моменти часу на основі значень властивостей артефактів. Правила виводу визначають зміну стану бізнес-процесу. Запропоновано представлення знань, що враховує стан контексту виконання дій бізнес-процесу у вигляді зважених логічних фактів, а також правил виводу, які забезпечують підтримку вибору дій з урахуванням поточного стану контексту. Відмінність запропонованого представлення полягає в тому, що при визначенні фактів враховуються атрибути подій, а правил – структура та послідовність подій логу бізнес-процесу. Також враховуються апріорні знання про предметну область у вигляді обмежень. Практичне значення представлення знань полягає у можливості автоматизованого виявлення причинно-наслідкових залежностей між діями бізнес-процесу у відповідності до інформації, що входить до складу його логу.The problem of constructing knowledge representation in the process control system for knowledge-capacious business processes in the aspect of reflecting the cause-effect relationships between the context of performance of actions and the actions of the business process is studied. It is shown that the general approach to solving this problem is related to the isolation of causal dependencies on the basis of an analysis of the event log containing records of the behavior of the business process. When solving the problem, problems of clarifying the structure of the representation of the cause-effect aspect of knowledge are formulated in accordance with the features of the log of the information system of process control; the construction of logical facts and rules in accordance with the structure of log events; formalization of knowledge representation taking into account facts, rules and a priori restrictions. It is established that the connection between the artifacts of the context and the events of the business process log is carried out through common attributes. Between artifacts and attributes and between events and attributes, there is one-to-many relationship. The logical component of the knowledge base is structured in the form of logical facts and rules. Logical facts determine the state of the business process at discrete points in time based on the values of the properties of artifacts. The output rules determine the change in the state of the business process. A knowledge representation is proposed that takes into account the state of the context for the execution of business process actions in the form of weighted logical facts, as well as output rules that support the selection of actions taking into account the current state of the context. The difference between the proposed representation is that when defining facts, the attributes of events are taken into account, and the rules are the structure and sequence of events of the business process log. A priori knowledge of the subject area in the form of constraints is also tak en into account. The practical importance of the knowledge representation is the ability to automatically identify the cause-effect relationships between the actions of the business process in accordance with the information in its log

Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.Comment: ICML2012, fixed citations to use correct tech report numbe

Structure Selection from Streaming Relational Data

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster

Probabilistic inductive constraint logic

AbstractProbabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for "probabilistic inductive constraint logic". Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements