A new approach for discovering business process models from event logs.

Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learnings since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered ascross-sectional.This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with time-varying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research; Business; Processes; Event; Information; Information systems; Systems; Learning; Data; Behavior; Patterns; IT; Event calculus; Knowledge; Database; Noise;

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Formal Specifications of Geographic Data Processing Requirements

This paper establishes a formal foundation for the specification of Geographic Data Processing (GDP) requirements. The emphasis is placed on modeling data and knowledge requirements rather than processing needs. A subset of first order logic is proposed as the principal means for constructing formalizations of the GDP requirements in a manner that is independent of the data representation. Requirements executability is achieved by selecting a subset of logic compatible with the inference mechanisms available in Prolog. GDP significant concepts such as time, space and accuracy have been added to the formalization without losing Prolog implementabilty or separation of concerns. Rules of reasoning about time, space and accuracy (based on positional, temporal and fuzzy logic) may be compactly stated in a subset of second order predicate calculus and may be easily modified to meet the particular needs of specific application. Multiple views of the data and knowledge may coexist in the same formalization. The feasibility of the approach has been established with the aid of a tentative Prolog implementation of the formalism. The implementation also provides the means for graphical rendering of logical information on a high resolution color display

A logic programming framework for modeling temporal objects

Published versio

Progression and Verification of Situation Calculus Agents with Bounded Beliefs

We investigate agents that have incomplete information and make decisions based on their beliefs expressed as situation calculus bounded action theories. Such theories have an infinite object domain, but the number of objects that belong to fluents at each time point is bounded by a given constant. Recently, it has been shown that verifying temporal properties over such theories is decidable. We take a first-person view and use the theory to capture what the agent believes about the domain of interest and the actions affecting it. In this paper, we study verification of temporal properties over online executions. These are executions resulting from agents performing only actions that are feasible according to their beliefs. To do so, we first examine progression, which captures belief state update resulting from actions in the situation calculus. We show that, for bounded action theories, progression, and hence belief states, can always be represented as a bounded first-order logic theory. Then, based on this result, we prove decidability of temporal verification over online executions for bounded action theories. © 2015 The Author(s

A connectionist representation of first-order formulae with dynamic variable binding

The relationship between symbolicism and connectionism has been one of the major issues in recent Artificial Intelligence research. An increasing number of researchers from each side have tried to adopt desirable characteristics of the other. These efforts have produced a number of different strategies for interfacing connectionist and sym¬ bolic AI. One of them is connectionist symbol processing which attempts to replicate symbol processing functionalities using connectionist components.In this direction, this thesis develops a connectionist inference architecture which per¬ forms standard symbolic inference on a subclass of first-order predicate calculus. Our primary interest is in understanding how formulas which are described in a limited form of first-order predicate calculus may be implemented using a connectionist archi¬ tecture. Our chosen knowledge representation scheme is a subset of first-order Horn clause expressions which is a set of universally quantified expressions in first-order predicate calculus. As a focus of attention we are developing techniques for compiling first-order Horn clause expressions into a connectionist network. This offers practical benefits but also forces limitations on the scope of the compiled system, since we tire, in fact, merging an interpreter into the connectionist networks. The compilation process has to take into account not only first-order Horn clause expressions themselves but also the strategy which we intend to use for drawing inferences from them. Thus, this thesis explores the extent to which this type of a translation can build a connectionist inference model to accommodate desired symbolic inference.This work first involves constructing efficient connectionist mechanisms to represent basic symbol components, dynamic bindings, basic symbolic inference procedures, and devising a set of algorithms which automatically translates input descriptions to neural networks using the above connectionist mechanisms. These connectionist mechanisms are built by taking an existing temporal synchrony mechanism and extending it further to obtain desirable features to represent and manipulate basic symbol structures. The existing synchrony mechanism represents dynamic bindings very efficiently using tem¬ poral synchronous activity between neuron elements but it has fundamental limitations in supporting standard symbolic inference. The extension addresses these limitations.The ability of the connectionist inference model was tested using various types of first order Horn clause expressions. The results showed that the proposed connectionist in¬ ference model was able to encode significant sets of first order Horn clause expressions and replicated basic symbolic styles of inference in a connectionist manner. The system successfully demonstrated not only forward chaining but also backward chaining over the networks encoding the input expressions. The results, however, also showed that implementing a connectionist mechanism for full unification among groups of unifying arguments in rules, are encoding some types of rules, is difficult to achieve in a con¬ nectionist manner needs additional mechanisms. In addition, some difficult issues such as encoding rules having recursive definitions remained untouched