Chiara Ghidini

Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business constraints during business process execution. At any point during an execution of a process, the user can define business constraints in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business constraint. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business constraints. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital.

Querying the Web of Data: A Formal Approach

The increasing amount of interlinked RDF data has finally made available the necessary building blocks for the web of data. This in turns makes it possible (and interesting) to query such a collection of graphs as an open and decentralized knowledge base. However, despite the fact that there are already implementations of query answering algorithms for the web of data, there is no formal characterization of what a satisfactory answer is expected to be. In this paper, we propose a preliminary model for such an open collection of graphs which goes beyond the standard single-graph RDF semantics, describes three different ways in which a query can be answered, and characterizes them semantically in terms of three incremental restrictions on the relation between the domain of interpretation of each single component graph.

Semantics-Based Aspect-Oriented Management of Exceptional Flows in Business Processes

Enriching business process models with semantic annotations that are taken from an ontology has become a crucial need in service provisioning, integration and composition, and business processes management. We represent semantically annotated business processes as part of an Web ontology lanuage knowledge base that formalizes the business process structure, the business domain, a set of criteria that describe correct semantic annotations, and a set of constraints that describe requirements on the business process itself. In this paper, we show how the Semantic Web representation and reasoning techniques can be 1) exploited by our aspect-oriented approach to modularize exception-handling (as well as other crosscutting) mechanisms and 2) effectively applied to formalize and automatically verify constraints on the management of exceptional flows (as well as other relevant flows) in business processes. The benefits of the Semantic Web and the aspect-oriented technologies are illustrated in a case study, where exceptional flows are modularized separately and managed at the semantic level due to the proposed approach.

A formal semantics for weighted ontology mappings

Ontology mappings are often assigned a weight or confidence factor by matchers. Nonetheless, few semantic accounts have been given so far for such weights. This paper presents a formal semantics for weighted mappings between different ontologies. It is based on a classificational interpretation of mappings: if O1 and O2 are two ontologies used to classify a common set X, then mappings between O1 and O2 are interpreted to encode how elements of X classified in the concepts of O1 are re-classified in the concepts of O2, and weights are interpreted to measure how precise and complete re-classifications are. This semantics is justifiable by extensional practice of ontology matching. It is a conservative extension of a semantics of crisp mappings. The paper also includes properties that relate mapping entailment with description logic constructors.

A Network Model Approach to Retrieval in the Semantic Web

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the Web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, we investigate how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. We present an associative retrieval model for the Semantic Web and evaluate if and to what extent the use of associative retrieval techniques increases retrieval performance. The evaluation of new retrieval paradigms, such as retrieval based on technology for the Semantic Web, presents an additional challenge since no off-the-shelf test corpora exist. Hence, we give a detailed description of the approach taken to evaluate the information retrieval service we have built.

Predictive Business Process Monitoring Framework with Hyperparameter Optimization

Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) traces will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal for a given ongoing trace. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset.

Distributed First Order Logic

Abstract Distributed First Order Logic (DFOL) has been introduced more than ten years ago with the purpose of formalising distributed knowledge-based systems, where knowledge about heterogeneous domains is scattered into a set of interconnected modules. DFOL formalises the knowledge contained in each module by means of first-order theories, and the interconnections between modules by means of special inference rules called bridge rules. Despite their restricted form in the original DFOL formulation, bridge rules have influenced several works in the areas of heterogeneous knowledge integration, modular knowledge representation, and schema/ontology matching. This, in turn, has fostered extensions and modifications of the original DFOL that have never been systematically described and published. This paper tackles the lack of a comprehensive description of DFOL by providing a systematic account of a completely revised and extended version of the logic, together with a sound and complete axiomatisation of a general form of bridge rules based on Natural Deduction. The resulting DFOL framework is then proposed as a clear formal tool for the representation of and reasoning about distributed knowledge and bridge rules.

Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions

Predictive process monitoring is concerned with predicting measures of interest for a running case (e.g., a business outcome or the remaining time) based on historical event logs. Most of the current predictive process monitoring approaches only consider intra-case information that comes from the case whose measures of interest one wishes to predict. However, in many systems, the outcome of a running case depends on the interplay of all cases that are being executed concurrently. For example, in many situations, running cases compete over scarce resources. In this paper, following standard predictive process monitoring approaches, we employ supervised machine learning for prediction. In particular, we present a method for feature encoding of process cases that relies on a bi-dimensional state space representation: the first dimension corresponds to intra-case dependencies, while the second dimension reflects inter-case dependencies to represent shared information among running cases. The inter-case encoding derives features based on the notion of case types that can be used to partition the event log into clusters of cases that share common characteristics. To demonstrate the usefulness and applicability of the method, we evaluated it against two real-life datasets coming from an Israeli emergency department process, and an open dataset of a manufacturing process.

Ontology authoring with FORZA

Generic, reusable ontology elements, such as a foundational ontologys categories and part-whole relations, are essential for good and interoperable knowledge representation. Ontology developers, which include domain experts and novices, face the challenge to figure out which category or relationship to choose for their ontology authoring task. To reduce this bottleneck, there is a need to have guidance to handle these Ontology-laden entities. We solve this with a generic approach and realize it with the Foundational Ontology and Reasoner-enhanced axiomatiZAtion (FORZA) method, containing DOLCE, a decision diagram for DOLCE categories, part-whole relations, and an automated reasoner that is used during the authoring process to propose feasible axioms. This fusion has been integrated in the MoKi ontology development tool to validate its implementability.

Abducing Compliance of Incomplete Event Logs

The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an end-to-end complete trace to be performed. This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach.