Development of knowledge representation based on markov logical networks in the business process mangement system

Досліджено проблему побудови представлення знань в системі процесного управління на основі аналізу поведінки бізнес-процесів, що представлена у вигляді логів подій. Кожна подія характеризує дію бізнес-процесу. Актуальність проблеми визначається тим, що при управлінні складними знання-ємними бізнес-процесами виконавці можуть змінювати послідовність дій з урахуванням додаткових знань про предметну область. В результаті виникає невідповідність між процесом та його моделлю, що створює труднощі для подальшого управління бізнес-процесом. Для усунення вказаної невідповідності потрібно формалізувати ці додаткові знання та використовувати їх при процесному управлінні, що потребує створення відповідного представлення знань. Запропоновано модель представлення знань враховує статичні й динамічні характеристики бізнес-процесу. Статичні характеристики бізнес-процесу задаються фактами та правилами із аргументами, представленими атрибутами подій логу. Факти і правила формуються на основі відповідних шаблонів. Атрибути задають значення властивостей об’єктів, з якими оперує бізнес-процес. Динамічні особливості бізнес-процесу визначаються через поточний розподіл ймовірностей виконання правил з урахуванням атрибутів поточної події логу бізнес-процесу. Запропонована модель відрізняється тим, що вона враховує обмеження на допустимі послідовності виконання дій бізнес-процесу, а також обмеження на основі апріорних знань про предметну область. Такі обмеження дозволить понизити складність задачі пошуку ймовірностей успішного завершення бізнес-процесу шляхом скорочення множини допустимих трас в тому випадку, якщо виконавці змінили послідовність дій. В практичному аспекті модель забезпечує можливість підтримки прийняття рішень з управління знання-ємними бізнес-процесами на основі прогнозування ймовірностей досягнення кінцевого стану процесу з урахуванням атрибутів подій логу.The problem of constructing knowledge representation in the process control system based on the analysis of the behavior of business processes, represented in the form of logs of events, is studied. Each event characterizes the action of the business process. The urgency of the problem is determined by the fact that when managing complex knowledge-capacious business processes, performers can change the sequence of actions taking into account additional knowledge about the subject area. As a result, there is a discrepancy between the process and its model, which creates difficulties for the further management of this business process. To eliminate this discrepancy, it is necessary to formalize the additional knowledge used and apply them in process management, which requires the creation of an appropriate knowledge representation. The proposed knowledge representation model takes into account the static and dynamic characteristics of the business process. The static characteristics of a business process are specified by facts and rules with arguments represented by the attributes of the log events. Facts and rules are formed on the basis of appropriate templates. Attributes specify the values of the properties of objects with which the business process operates. Dynamic features of the business process are determined through the current distribution of the probability that the rules will be executed, taking into account the attributes of the current business process log event. The proposed model is characterized by the fact that it takes into account the limitations on the permissible sequences of execution of the actions of the business process, as well as restrictions based on a priori knowledge of the subject area. Such restrictions will reduce the complexity of the problem of finding the probabilities of a successful completion of a business process by reducing the number of allowed trails in the event that the performers have changed the sequence of actions. In practical terms, the model provides the ability to support decision-making on the management of knowledge-intensive business processes based on predicting the probabilities of achieving the final state of the process, taking into account the attributes of log events

Learning Tuple Probabilities

Learning the parameters of complex probabilistic-relational models from labeled training data is a standard technique in machine learning, which has been intensively studied in the subfield of Statistical Relational Learning (SRL), but---so far---this is still an under-investigated topic in the context of Probabilistic Databases (PDBs). In this paper, we focus on learning the probability values of base tuples in a PDB from labeled lineage formulas. The resulting learning problem can be viewed as the inverse problem to confidence computations in PDBs: given a set of labeled query answers, learn the probability values of the base tuples, such that the marginal probabilities of the query answers again yield in the assigned probability labels. We analyze the learning problem from a theoretical perspective, cast it into an optimization problem, and provide an algorithm based on stochastic gradient descent. Finally, we conclude by an experimental evaluation on three real-world and one synthetic dataset, thus comparing our approach to various techniques from SRL, reasoning in information extraction, and optimization

Querying and Learning in Probabilistic Databases

Abstract. Probabilistic Databases (PDBs) lie at the expressive inter-section of databases, first-order logic, and probability theory. PDBs em-ploy logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query an-swers via logical lineage formulas (aka.“data provenance”) to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for es-tablishing a closed and complete representation model of relational op-erations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabilistic inference, the latter of which remains a #P-hard problem also in the context of PDBs. In this chapter, we provide a review on the key concepts of PDBs with a particular focus on our own recent research results related to this field. We highlight a number of ongoing research challenges related to PDBs, and we keep referring to an information extraction (IE) scenario as a running application to manage uncertain and temporal facts obtained from IE techniques directly inside a PDB setting

Querying and Learning in Probabilistic Databases

Probabilistic Databases (PDBs) lie at the expressive intersection of databases, first-order logic, and probability theory. PDBs employ logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query answers via logical lineage formulas (aka. "data provenance") to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for establishing a closed and complete representation model of relational operations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabilistic inference, the latter of which remains a #P-hard problem also in the context of PDBs. In this chapter, we provide a review on the key concepts of PDBs with a particular focus on our own recent research results related to this field. We highlight a number of ongoing research challenges related to PDBs, and we keep referring to an information extraction (IE) scenario as a running application to manage uncertain and temporal facts obtained from IE techniques directly inside a PDB setting