Giorgio Leonardi

Retrieval and clustering for supporting business process adjustment and analysis

In this paper, we describe a framework able to support run-time adjustment and a posteriori analysis of business processes, which exploits the retrieval step of the Case-based Reasoning (CBR) methodology. In particular, our framework allows to retrieve traces of process execution similar to the current one. Moreover, it supports an automatic organization of the trace database content through the application of hierarchical clustering techniques. Results can provide help both to end users, in the process execution phase, and to process engineers, in (formal) process conformance evaluation and long term process schema redesign. Retrieval and clustering rely on a distance definition able to take into account temporal information in traces. This metric has outperformed simpler distance definitions in our experiments, which were conducted in a real-world application domain.

Interacting agents through a web-based health serviceflow management system

The management of chronic and out-patients is a complex process which requires the cooperation of different agents belonging to several organizational units. Patients have to move to different locations to access the necessary services and to communicate their health status data. From their point of view there should be only one organization (Virtual Health-Care Organization) which provides both virtual and face-to-face encounters. In this paper we propose the Serviceflow Management System as a solution to handle these information and the communication requirements. The system consists of: (a) the model of the care process represented as a Serviceflow and developed using the Workflow Management System YAWL; (b) an organizational ontology representing the VHCO; and (c) agreements and commitments between the parties defined in a contract (represented as an XML document). On the basis of a general architecture we present an implementation in the area of Diabetes management.

A Case-Based Architecture for Temporal Abstraction Configuration and Processing

In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextual knowledge plays a fundamental role in the time series interpretation. Since defining the right configuration for each possible contextual situation may be impractical, we propose to adopt a case-based approach, where the suitable configuration can be obtained by looking at the most similar already configured case, with respect to the current situation. Configured cases are indexed by means of contextual information. The obtained configuration can then be used as input to a temporal abstraction module, providing a set of qualitative states, trends and suitable combination of both as a result. Cases can then be exploited in the processing of such results as well, by providing an evaluation of the whole abstraction processing, possibly leading to the revision of the case base. The approach is illustrated by means of an example taken from a medical application, concerning the monitoring and evaluation of patients undergoing hemodialysis treatment

RHENE: A Case Retrieval System for Hemodialysis Cases with Dynamically Monitored Parameters

In this paper, we present a case-based retrieval system called Rhene (Retrieval of HEmodialysis in NEphrological disorders) working in the domain of patients affected by nephropatologies and treated with hemodialysis. Defining a dialysis session as a case, retrieval of past similar cases has to operate both on static and on dynamic (time-dependent) features, since most of the monitoring variables of a dialysis session are time series. In Rhene, retrieval relies upon a multi-step procedure. In particular, a preliminary grouping/classification step, based on static features, reduces the retrieval search space. Intra-class retrieval then takes place by considering time-dependent features, and is articulated as follows: (1) “locally” similar cases (considering one feature at a time) are extracted and the intersection of the retrieved sets is computed; (2) “global” similarity is computed – as a weighted average of local distances – and the best cases are listed. The main goal of the paper is to present an approach for efficiently implementing step (2), by taking into account specific information regarding the final application. We concentrate on a classical dimensionality reduction technique for time series allowing for efficient indexing, namely Discrete Fourier Transform (DFT). Thanks to specific index structures (i.e. k-d trees) range queries (on local feature similarity) can be efficiently performed on our case base; as mentioned above, results of such local queries are then suitably combined, allowing the physician to examine the most similar stored dialysis sessions with respect to the current one and to assess the quality of the overall hemodialysis service.

Retrieval and Clustering for Business Process Monitoring: Results and Improvements

Business process monitoring is a set of activities for organizing process instance logs and for highlighting non-compliances and adaptations with respect to the default process schema. Such activities typically serve as the starting point for a-posteriori log analyses.

A Case-Based Approach to Business Process Monitoring

The agile workflow technology deals with flexible workflow adaptation and overriding, in case of foreseen as well as unforeseen changes and problems in the operating business environment. One key issue that an agile workflow system should address is Business Process (BP) monitoring. This consists in properly highlighting and organizing non-compliances and adaptations with respect to the default process schema. Such an activity can be the starting point for other very critical tasks, such as quality assessment and process reengineering.

Improving structural medical process comparison by exploiting domain knowledge and mined information

OBJECTIVES Process model comparison and similar process retrieval is a key issue to be addressed in many real-world situations, and a particularly relevant one in medical applications, where similarity quantification can be exploited to accomplish goals such as conformance checking, local process adaptation analysis, and hospital ranking. In this paper, we present a framework that allows the user to: (i) mine the actual process model from a database of process execution traces available at a given hospital; and (ii) compare (mined) process models. The tool is currently being applied in stroke management. METHODS Our framework relies on process mining to extract process-related information (i.e., process models) from data. As for process comparison, we have modified a state-of-the-art structural similarity metric by exploiting: (i) domain knowledge; (ii) process mining outputs and statistical temporal information. These changes were meant to make the metric more suited to the medical domain. RESULTS Experimental results showed that our metric outperforms the original one, and generated output closer than that provided by a stroke management expert. In particular, our metric correctly rated 11 out of 15 mined hospital models with respect to a given query. On the other hand, the original metric correctly rated only 7 out of 15 models. The experiments also showed that the framework can support stroke management experts in answering key research questions: in particular, average patient improvement decreased as the distance (according to our metric) from the top level hospital process model increased. CONCLUSIONS The paper shows that process mining and process comparison, through a similarity metric tailored to medical applications, can be applied successfully to clinical data to gain a better understanding of different medical processes adopted by different hospitals, and of their impact on clinical outcomes. In the future, we plan to make our metric even more general and efficient, by explicitly considering various methodological and technological extensions. We will also test the framework in different domains.

Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multi-level abstractions , according to two dimensions , namely a taxonomy of (trend or state) symbols, and a variety of time granularities. Moreover, we allow for flexible querying , where queries can be expressed at any level of detail in both dimensions, also in an interactive fashion, and ground cases as well as generalized ones can be retrieved. We also take advantage of multi-dimensional orthogonal index structures , which can be refined progressively and on demand . The framework in practice is illustrated by means of a case study in hemodialysis.

A CBR-BASED, CLOSED-LOOP ARCHITECTURE FOR TEMPORAL ABSTRACTIONS CONFIGURATION

In the hemodialysis domain, we are implementing a case‐based, closed‐loop architecture aimed at configuring temporal abstractions (TA), which will be applied to time series data. The advantage of a case‐based approach is the one of “quickly” obtaining a suitable TA parameter configuration, simply by looking at the most similar already configured case, where configured cases are indexed by means of contextual information. The retrieved configuration, together with the time series data, is then used as an input to a TA processing module, able to provide a set of qualitative states, trends, and significant combinations of both as an output. TA processing results can finally be evaluated, possibly leading to a (human‐supervized) reorganization/revision of the case base content, to ameliorate future TA configuration sessions—thus closing the loop. The work is being integrated with RHENE, a system for case‐based retrieval in hemodialysis, able to work both on raw time series data and on preprocessed (by means of TA) ones.

Mining and Retrieving Medical Processes to Assess the Quality of Care

In a competitive healthcare market, hospitals have to focus on ways to deliver high quality care while at the same time reducing costs. To accomplish this goal, hospital managers need a thorough understanding of the actual processes. Process mining can be used to extract process related information (e.g., process models) from data. This process information can be exploited to understand and redesign processes to become efficient high quality processes. Process analysis and redesign can take advantage of Case Based Reasoning techniques.