Carlo Combi

Capturing Temporal Constraints in Temporal ER Models

A wide range of database applications manage information that varies over time. The conceptual modeling of databases is frequently based on one of the several versions of the ER model. As this model does not provide built-in means for capturing temporal aspects of data, the resulting diagrams are unnecessarily obscure and inadequate for documentation purposes. The TimeER model extends the ER model with suitable constructs for modeling time-varying information, easing the design process, and leading to easy-to-understand diagrams. In a temporal ER model, support for the specification of advanced temporal constraints would be desiderable, allowing the designer to specify, e.g., that the value of an attribute must not change over time. This paper extends the TimeER model by introducing the notation, and the associated semantics, for the specification of new temporal constraints.

Temporal reasoning and temporal data maintenance in medicine: issues and challenges.

We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medicine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.

Data mining with Temporal Abstractions: learning rules from time series

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.

Position paper: Temporal representation and reasoning in medicine: Research directions and challenges

OBJECTIVE The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.

Representing and Reasoning about Temporal Granularities

In this paper, we propose a new logical approach to represent and to reason about different time granularities. We identify a time granularity as an infinite sequence of time points properly labelled with proposition symbols marking the starting and ending points of the corresponding granules, and we symbolically model sets of granularities by means of linear time logic formulas. Some real-world granularities are provided, from a clinical domain and from the Gregorian Calendar, to motivate and exemplify our approach. Different formulas are introduced, which represent relations between different granularities. The proposed framework permits one to algorithmically solve the consistency, the equivalence, and the classification problems in a uniform way, by reducing them to the validity problem for the considered linear time logic.

Data mining on temporal data: a visual approach and its clinical application to hemodialysis

Abstract The quantity and complexity of data acquired, time-stamped and stored in clinical databases by automated medical devices is rapidly and continuously increasing. As a result, it becomes more and more important to provide clinicians with easy-to-use interactive tools to analyze huge amounts of this data. This paper proposes an approach for visual data mining on temporal data and applies it to a real medical problem, i.e. the management of hemodialysis. The approach is based on the integration of 3D and 2D information visualization techniques and offers a set of interactive functionalities that will be described in detail in the paper. We will also discuss how the system has been evaluated with end users and how the evaluation led to changes in system design.

Temporal Information Systems in Medicine

The effective and efficient use of information systems in health care organizations and services are a vital element of the quality of life world wide. The modeling of time has attracted interest from a number of research communities, each addressing aspects from its specific sphere of interests. The database systems community is interested in how to store, maintain, and query time-oriented data. The Artificial Intelligence (AI) community is interested in formal theories of time, temporal logics, temporal constraints and temporal reasoning techniques. The Artificial Intelligence in Medicine (AIM) community is interested in the broader issues. Temporal Information Systems in Medicine introduces the engineering of information systems for medically-related problems and applications. The chapters are organized into four parts; fundamentals, temporal reasoning & maintenance in medicine, time in clinical task, and the display of time-oriented clinical information. The chapters are self-contained with pointers to other relevant chapters or sections in this book when necessary. Time is of central importance and is a key component of the engineering process for information systems. About this book Presents a cutting edge text on Temporal Information Systems in Medicine. Chapters are self contained with pointers to other relevant material or chapters in this book. Serves as an introduction to a reader interested in a general theoretical understanding of temporal representation and reasoning, especially as it relates to medicine, and as a source for those interested in specific clinical time-oriented tasks, such as diagnosis or abstraction of clinical records. This book is designed as a secondary text or reference book for upper -undergraduate level students and graduate level students concentrating on computer science, biomedicine and engineering. Industry professionals and researchers working in health care management, temporal information systems in medicine, medical informatics, database management and AI will also find this book a valuable asset.

Visualizing queries on databases of temporal histories: new metaphors and their evaluation

A crucial component for turning any temporal reasoning system into a real-world application that can be adopted by a wide base of users is given by its user interface. After analyzing and discussing the state of the art for the visualization of temporal intervals and relations, this paper proposes three new solutions to the problem of visualizing temporal intervals and their relations for querying databases containing several histories. The metaphors exploited in the proposed visual vocabularies are based on real-world, concrete objects, such as strips, springs, weights, and wires. We discuss the expressivity of the visual vocabularies with respect to the well-known Allens Interval Algebra. A method for mapping queries composed by the visual vocabularies into SQL queries is then described and discussed. The proposed solutions were evaluated with two proper user studies: the first focused on determining which of the adopted metaphors are more frequently perceived and understood in a correct way and was based on a questionnaire; the second considered the two solutions which scored better in the first phase and studied them with a more thorough experiment, which was also based on user interfaces implementing the two proposals. The visual vocabulary which provided the best results has been adopted in a medical system for visual querying clinical temporal databases.

Temporal similarity measures for querying clinical workflows

OBJECTIVE In this paper, we extend a preliminary proposal and discuss in a deeper and more formal way an approach to evaluate temporal similarity between clinical workflow cases (i.e., executions of clinical processes). More precisely, we focus on (i) the representation of clinical processes by using a temporal conceptual workflow model; (ii) the definition of ad hoc temporal constraint networks to formally represent clinical workflow cases; (iii) the definition of temporal similarity for clinical workflow cases based on the comparison of temporal constraint networks; (iv) the management of the similarity of clinical processes related to the Italian guideline for stroke prevention and management (SPREAD). BACKGROUND Clinical processes are composed by clinical activities to be done by given actors in a given order satisfying given temporal constraints. This description means that clinical processes can be seen as organizational processes, and modeled by workflow schemata. When a workflow schema represents a clinical process, its cases represent different instances derived from dealing with different patients in different situations. With respect to all the cases related to a workflow schema, each clinical case can be different with respect to its structure and to its temporal aspects. Clinical cases can be stored in clinical databases and information retrieval can be done evaluating the similarity between workflow cases. METHODOLOGY We first describe a possible approach to the conceptual modeling of a clinical process, by using a temporally extended workflow model. Then, we define how a workflow case can be represented as a set of activities, and show how to express them through temporal constraint networks. Once we have built temporal constraint networks related to the cases to compare, we propose a similarity function able to evaluate the differences between the considered cases with respect to the order and duration of corresponding activities, and with respect to the presence/absence of some activities. RESULTS In this work, we propose an approach to evaluate temporal similarity between workflow cases. The proposed approach can be used (i) to query clinical databases storing clinical cases representing activities related to the management of different patients in different situations; (ii) to evaluate the quality of the service comparing the similarity between a (possibly synthetic) case, perceived as the good one with respect to a given clinical situation, and the other clinical cases; and (iii) to retrieve a particular class of cases similar to an interesting one.

Controllability in Temporal Conceptual Workflow Schemata

Workflow technology has emerged as one of the leading technologies in modelling, redesigning, and executing business processes. Currently available workflow management systems (WfMS ) and research prototypes offer a very limited support for the definition, detection, and management of temporal constraints over business processes. In this paper, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and, in particular, we introduce and discuss the concept of controllability for workflow schemata and its evaluation at process design time. Controllability refers to the capability of executing a workflow for any possible duration of tasks. Since in several situations durations of tasks cannot be decided by WfMSs , even tough the minimum and the maximum durations for each task are known, checking controllability is stronger than verifying the consistency of the workflow temporal constraints.