Ario Santoso

Ontology-based governance of data-aware processes

In this paper we show how one can use the technology developed recently for Ontology-Based Data Access (OBDA) to govern data-aware processes through ontologies. In particular, we consider processes executed over a relational database which issue calls to external services to acquire new information and update the data.We equip these processes with an OBDA system, in which an ontology modeling the domain of interest is connected through declarative mappings to the database, and that consequently allows one to understand and govern the manipulated information at the conceptual level. In this setting, we are interested in verifying first-order μ-calculus formulae specifying temporal properties over the evolution of the information at the conceptual level. Specifically, we show how, building on first-order rewritability of queries over the system state that is typical of OBDA, we are able to reformulate the temporal properties into temporal properties expressed over the underlying database. This allows us to adopt notable decidability results on verification of evolving databases that have been established recently.

Verification of Semantically-Enhanced Artifact Systems

Artifact-Centric systems have emerged in the last years as a suitable framework to model business-relevant entities, by combining their static and dynamic aspects. In particular, the Guard-Stage-Milestone GSM approach has been recently proposed to model artifacts and their lifecycle in a declarative way. In this paper, we enhance GSM with a Semantic Layer, constituted by a fullfledged OWL 2 QL ontology linked to the artifact information models through mapping specifications. The ontology provides a conceptual view of the domain under study, and allows one to understand the evolution of the artifact system at a higher level of abstraction. In this setting, we present a technique to specify temporal properties expressed over the Semantic Layer, and verify them according to the evolution in the underlying GSM model. This technique has been implemented in a tool that exploits state-of-the-art ontology-based data access technologies to manipulate the temporal properties according to the ontology and the mappings, and that relies on the GSMC model checker for verification.

Verification of Context-Sensitive Knowledge and Action Bases

Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution. In this work, we enrich the KAB setting with contextual information, making use of different context dimensions. On the one hand, context is determined by the environment using context-changing actions that make use of the current state of the KB and the current context. On the other hand, it affects the set of TBox assertions that are relevant at each time point, and that have to be considered when processing queries posed over the KAB. Here we extend to our enriched setting the results on verification of rich temporal properties expressed in μ-calculus, which had been established for standard KABs. Specifically, we show that under a run-boundedness condition, verification stays decidable and does not incur in any additional cost in terms of worst-case complexity.We also show how to adapt syntactic conditions ensuring run-boundedness so as to account for contextual information, taking into account context-dependent activation of TBox assertions.

OBDA for Log Extraction in Process Mining

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. Through process mining, decision makers can discover process models from data, compare expected and actual behaviors, and enrich models with key information about their actual execution. To be applicable, process mining techniques require the input data to be explicitly structured in the form of an event log, which lists when and by whom different case objects (i.e., process instances) have been subject to the execution of tasks. Unfortunately, in many real world set-ups, such event logs are not explicitly given, but are instead implicitly represented in legacy information systems. To apply process mining in this widespread setting, there is a pressing need for techniques able to support various process stakeholders in data preparation and log extraction from legacy information systems. The purpose of this paper is to single out this challenging, open issue, and didactically introduce how techniques from intelligent data management, and in particular ontology-based data access, provide a viable solution with a solid theoretical basis.

When data, knowledge and processes meet together

The aim of this work is to investigate the integration of data, knowledge, and processes. In particular, we study the combination of research in Ontology-Based Data Access (OBDA) with Data- Centric Dynamic Systems (DCDSs). The idea of OBDA is to combine data and knowledge by providing a conceptual view over data repositories in terms of an ontology, while DCDSs provide a holistic framework for modeling business processes in which both data and processes are treated as first-class citizens. Thanks to this combination, we obtain Semantically-Governed Data-Aware Processes (SGDAPs), which represent a significant step towards the envisioned unifying framework. We position SGDAPs in the state of the art and briefly discuss the current status of our research. We then identify several research directions along which we intend to continue our work.