Abstract This paper presents , a novel distance between time series based on Random Forests (RFs). We extend to the time-series domain concepts and tools of RF distances, a recent class of robust data-dependent distances defined for vectorial representations, thus proposing the first RF distance for time series. The distance is determined by (i) creating an RF to model a set of time series, and (ii) exploiting the trained RF to quantify the similarity between time series. As for the first step, we introduce in this paper the Extremely Randomized Canonical Interval Forest (ERCIF), a novel extension of Canonical Interval Forests that can model time series and can be trained without labels. We then exploit three different schemes, following ideas already employed in the vectorial case. The proposed distance, in different variants, has been thoroughly evaluated with 128 datasets from the archive, showing promising results compared with literature alternatives.

Simple temporal networks with uncertainty (STNUs) have achieved wide attention and are the basis of many applications requiring the representation of temporal constraints and checking whether they are conflicting. Dynamic controllability is currently the most studied notion to check whether a system can be controlled without violating temporal constraints despite uncertainties. However, dynamic controllability assumes that the actual duration of a contingent activity is known only when the end event of that activity occurs. The recently introduced notion of agile controllability considers the case where this duration is known earlier, leading to a more relaxed notion of temporal feasibility. We extend the definition of STNUs to STNUOs (Simple Temporal Networks with Uncertainty and Oracles) to represent the point in time at which information about a contingent duration is available. We formally define agile controllability as a generalization of dynamic controllability considering the timepoints of information availability. We propose a set of constraint propagation rules for STNUOs, leading to an algorithm for checking agile controllability.

In this paper, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework and on a multi-temporal relational model. Different features are proposed for the Observation Window (OW), where we observe predictive data, for the Waiting Window (WW), and for the Prediction Window (PW), where the predicted event occurs. We then consider the concept of approximation for such APFDs, introduce new error measures, and discuss different strategies for deriving APFDs. We discuss the quality, i.e., the informative content, of the derived AFDs by considering their entropy and information gain. Moreover, we outline the results in deriving APFDs focusing on the Acute Kidney Injury (AKI). We use real clinical data contained in the MIMIC III dataset related to patients from Intensive Care Units to show the applicability of our approach to real-world data.

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Managing temporal process constraints in modularized processes is an important task, both during the design, as it allows the reuse of temporal (child) process models, and during the checking of temporal properties of processes, as it avoids the necessity of “unfolding” child processes within the main process model. Taking into account the capability of providing modular solutions, modeling and checking temporal features of processes is still an open problem in the context of process-aware information systems. In this paper, we present and discuss a novel approach to represent flexible temporal constraints in modularized time-aware BPMN process models. To support temporal flexibility, allowed task durations are represented through guarded ranges that allow a limited (guarded) restriction of task durations during process execution if it is necessary to guarantee the satisfaction of all temporal constraints. We, then, propose how to derive a compact representation of the overall temporal behavior of such time-aware BPMN models. Such compact representation of child processes allows us to check the dynamic controllability (DC) of a parent time-aware process model without “unfolding” the child process models. Dynamic controllability guarantees that process models can have process instances (i.e., executions) satisfying all the temporal constraints for any possible combination of allowed durations of tasks and child processes. Possible approaches for even more flexibility by solving some kinds of DC violations are then introduced. We use a real process model from a healthcare domain as a motivating example, and we also present a proof-of-concept prototype confirming the concrete applicability of the solutions we propose, followed by an experimental evaluation.

Modeling and reasoning over business processes require enterprises to manage and integrate} large amounts of information. Despite process designers and engineers may benefit from a unified view of process and data models, integrating these two perspectives is challenging, especially when considering conceptual models. In this paper, we provide a uniform formal representation of a process model, the schema of a related database, and the data operations connecting them. Then, we show how we can use such a formal representation to identify interesting information during the integrated conceptual modeling and analysis of processes and related databases, from a process (re-)design and improvement perspective. Finally, we discuss the evaluation of the proposed approach through a controlled experiment and a proof-of-concept implementation that considers both relational and XML database technologies.

Illegal recruitment in agriculture is an issue that affects many different aspects, from the workers’ physical and psychological health conditions to the overall economy. This phenomenon is particularly complex, and many disciplines are trying to face it. In the domain of computer science, one of the possibilities is to improve the systems used by the recruitment/temp agencies. In this study, we propose FARmAPP, a process-driven tool used by the recruitment agencies and farms. FARmAPP was developed using an agile approach with a direct contribution from three different recruitment agencies that operates in three Italian regions. FARmAPP collects and analyzes usage data to monitor “suspect” behaviors from the farms that could lead back to illegal recruitment or workers exploitation. We also created a new custom algorithm to analyze the CVs of the unemployed people to suggest the best candidates for each different job. After the development of FARmAPP, we trained over 80 agencies employees to use and manage FARmAPP autonomously. Their feedback was overall positive, and they stated that FARmAPP is a helpful tool to be included in their system when dealing with agricultural jobs.

This is an extended abstract of our article1accepted for publication in the IEEE Transactions on Services Computing Journal proposing an approach for the conceptual modeling and design-time analysis of the data operations performed by a business process on a database. Our approach leverages a uniform formal representation that captures a process model, a conceptual database schema, and the data operations performed by the process on the database. We then show how such a formal model can be queried to obtain insights into the informational perspective of business process models and, in turn, support process modelers and analysts in reasoning about process (re) design and improvement. The approach is evaluated with a controlled experiment and a proof-of-concept implementation.1This extended abstract summarizes the approach and selected findings of the work referenced as [1] with doi:10.1109/TSC.2021.3134485

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.

A Flexible Simple Temporal Network with Uncertainty (FTNU) represents temporal constraints between time-points. Time-points are variables that must be set (executed) satisfying all the constraints. Some time-points are contingent. It means that they are set by the environment and only observed by the system executing the network. The ranges representing temporal constraints associated with contingent time-points (guarded ranges) can be shrunk during execution only to some extent to have more flexibility in the execution of the network. Subsets of time-points/constraints may be executed/considered in different contexts according to some observed conditions. The main issue here consists of determining whether all the time-points, under the control of the system, are executable in a way that all the specified constraints are satisfied for any possible occurrence of contingent time-points and any possible context. Such property is called controllability. Even though an algorithm was proposed for checking the controllability of such networks, we show that such an algorithm has a limit. Indeed, it does not determine the right bounds for guarded links, and, therefore, it doesn’t permit the system to exploit the potential flexibility of the network. We then propose a new constraint-propagation algorithm for checking controllability, prove that such a new algorithm determines the right guarded ranges, and it is sound-and-complete. Thus, it can be used also for executing the network, by leveraging its flexibility.

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, and is associated with worse short and long-term outcomes. An early prediction of AKI to detect the patients at risk could be a first step in the discovery and assessment of new therapies, and in improvements of patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed the development of predictive models of AKI diagnosis. In this research work we provide a consistent reproducible ETL pipeline for Intensive Care Unit (ICU) data, in particular regarding the MIMIC III database, to support the early prediction of AKI. Then, we build different predictive models aimed at early identifying subjects who could experience AKI syndrome in their next 7 days after the ICU admission. The entire procedure is based on a recently proposed rolling observational window approach. We consider two predictive models, Gradient Boosting Decision Trees and Support Vector Machines, via different platforms.

Biologic drugs have revolutionized the treatments in many therapeutic areas and their pre and post-marketing evaluation is crucial for investigating their benefit-risk profile. The VALORE project is an AIFA (Italian Medicines Agency)-funded research project aiming at establishing a multi-regional network for the integrated analysis of healthcare and clinical data from different sources. Here we deal with the design and implementation of a data mart supporting the multidimensional data analysis. We also discuss the design of a portable architecture, making each regional center autonomous with respect to the required analysis. It is a first step in the direction of providing all the regional centers and AIFA with a common tool for the analysis and monitoring of marketed biologic drugs.

In the field of Business Process Management (BPM), modeling business processes and related data is a critical issue since process activities need to manage data stored in databases. The connection between processes and data is usually handled at the implementation level, even if modeling both processes and data at the conceptual level should help designers in improving business process models and identifying requirements for implementation. Especially in data- and decision-intensive contexts, business process activities need to access data stored both in databases and data warehouses. In this paper, we complete our approach for defining a novel conceptual view that bridges process activities and data. The proposed approach allows the designer to model the connection between business processes and database models and define the operations to perform, providing interesting insights on the overall connected perspective and hints for identifying activities that are crucial for decision support.

Extracting association rules from large datasets has been widely studied in many variants in the last two decades; they allow to extract relations between values that occur more “often” in a database. With temporal association rules the concept has been declined to temporal databases. In this context the “most frequent” patterns of evolution of one or more attribute values are extracted. In the temporal setting, especially where the interference betweeen temporal patterns cannot be neglected (e.g., in medical domains), there may be the case that we are looking for a set of temporal association rules for which a “significant” portion of the original database represents a consistent model for all of them. In this work, we introduce a simple and intuitive form for temporal association rules, called pure evolving association rules (PE-ARs for short), and we study the complexity of checking a set of PE-ARs over an instance of a temporal relation under approximation (i.e., a percentage of tuples that may be deleted from the original relation). As a by-product of our study we address the complexity class for a general problem on Directed Acyclic Graphs which is theoretically interesting per se.

BPMN (Business Process Model and Notation) is widely used standard modeling technique for representing Business Processes by using diagrams, but lacks in some aspects. Representing execution-dependent and time-dependent decisions in BPMN Diagrams may be a daunting challenge [Carlo Combi et al., 2017]. In many cases such constraints are omitted in order to preserve the simplicity and the readability of the process model. However, for purposes such as compliance checking, process mining, and verification, formalizing such constraints could be very useful. In this paper, we propose a novel approach for annotating BPMN Diagrams with Temporal Synchronization Rules borrowed from the timeline-based planning field. We discuss the expressivity of the proposed approach and show that it is able to capture a lot of complex temporally-related constraints without affecting the structure of BPMN diagrams. Finally, we provide a mapping from annotated BPMN diagrams to timeline-based planning problems that allows one to take advantage of the last twenty years of theoretical and practical developments in the field.

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN by adding decision time points to model temporal plans with decisions. A decision time point is a special kind of time point that once executed allows for deciding a truth value for an associated Boolean proposition. Furthermore, STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. Thus, an STND models a family of STNs each obtained as a projection of the initial STND onto a scenario. An STND is consistent if there exists a consistent scenario (i.e., a scenario such that the corresponding STN projection is consistent). Recently, a hybrid SAT-based consistency checking algorithm (HSCC) was proposed to check the consistency of an STND. Unfortunately, that approach lacks experimental evaluation and does not allow for the synthesis of all consistent scenarios. In this paper, we propose an incremental HSCC algorithm for STNDs that (i) is faster than the previous one and (ii) allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). Then, we carry out an experimental evaluation with KAPPA, a tool that we developed for STNDs. Finally, we prove that STNDs and disjunctive temporal networks (DTNs) are equivalent.

Conditional simple temporal networks with uncertainty (CSTNUs) allow for the representation of temporal plans subject to both conditional constraints and uncertain durations. Dynamic controllability (DC) of CSTNUs ensures the existence of an execution strategy able to execute the network in real time (i.e., scheduling the time points under control) depending on how these two uncontrollable parts behave. However, CSTNUs do not deal with resources. In this paper, we define conditional simple temporal networks with uncertainty and resources (CSTNURs) by injecting resources and runtime resource constraints (RRCs) into the specification. Resources are mandatory for executing the time points and their availability is represented through temporal expressions, whereas RRCs restrict resource availability by further temporal constraints among resources. We provide a fully-automated encoding to translate any CSTNUR into an equivalent timed game automaton in polynomial time for a sound and complete DC-checking.

Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.

The proper handling of temporal constraints is crucial in many domains. As a particular challenge, temporal constraints must be also handled when different specific situations happen (conditional constraints) and when some event occurrences can be only observed at run time (contingent constraints). In this paper we introduce Conditional Simple Temporal Networks with Partially Shrinkable Uncertainty (CSTNPSUs), in which contingent constraints are made more flexible (guarded constraints) and they are also specified as conditional constraints. It turns out that guarded constraints require the ability to reason on both kinds of constraints in a seamless way. In particular, we discuss CSTNPSU features through a motivating example and, then, we introduce the concept of controllability for such networks and the related sound checking algorithm.

We define Access-Controlled Temporal Networks (ACTNs) as an extension of Conditional Simple Temporal Networks with Uncertainty (CSTNUs). CSTNUs are able to handle features such as contingent durations and conditional constraints, and have thus been used to model the temporal constraints of workflows underlying business processes. However, CSTNUs are unable to model users and authorization constraints, and thus cannot model "who can do what, when". ACTNs solve this problem by adding users and authorization constraints that must be considered together with temporal constraints. Dynamic controllability (DC) of ACTNs ensures the existence of an execution strategy, able to assign tasks to authorized users dynamically, satisfying all the relevant authorization constraints no matter what contingent durations turn out to be or what conditional constraints have to be considered. We show that the DC checking can be done via Timed Game Automata and provide experimental results using UPPAAL-TIGA on a concrete real-world case study.

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include special time-points, called observation time-points. In a CSTN, the agent executing the network controls the execution of every time-point. However, each observation time-point has a unique propositional letter associated with it and, when the agent executes that time-point, the environment assigns a truth value to the corresponding letter. Thus, the agent observes but, does not control the assignment of truth values. A CSTN is dynamically consistent (DC) if there exists a strategy for executing its time-points such that all relevant constraints will be satisfied no matter which truth values the environment assigns to the propositional letters. Alternatively, in a Labeled Simple Temporal Network (Labeled STN) - also called a Temporal Plan with Choice - the agent executing the network controls the assignment of values to the so-called choice variables. Furthermore, the agent can make those assignments at any time. For this reason, a Labeled STN is equivalent to a Disjunctive Temporal Network. This paper incorporates both of the above extensions by augmenting a CSTN to include not only observation time-points but also decision time-points. A decision time-point is like an observation time-point in that it has an associated propositional letter whose value is determined when the decision time-point is executed. It differs in that the agent - not the environment - selects that value. The resulting network is called a CSTN with Decisions (CSTND). This paper shows that a CSTND generalizes both CSTNs and Labeled STNs, and proves that the problem of determining whether any given CSTND is dynamically consistent is PSPACE-complete. It also presents algorithms that address two sub-classes of CSTNDs: (1) those that contain only decision time-points; and (2) those in which all decisions are made before execution begins.

Temporal Functional Dependencies (TFDs for short) are functional dependencies that predicate on temporal databases characterized by a special temporal dimension called valid time (VT). In [1] Combi et al. proposed a uniform framework that subsumes many of the TFDs proposed in literature and, by the combination of them, allow us to express finer constraints. Some interesting constraints are the Temporally Mixed Functional Dependencies (TMFD for short) that allow one to write constraints on the evolution of the data in the database. The problem of checking a TMFD against an instance of a temporal schema is polynomial. We will show that when approximation comes into play (i.e., we look for TMFD holding for almost all database tuples) the problem turns out to be NP-Complete. Moreover we introduce a type of association rules build over TMFD called Temporally Mixed Association Rule (TMAR). We prove that verifying TMAR under approximation is still NP-Complete, by reducing it to a novel problem on directed acyclic graphs.

A Conditional Simple Temporal Network (CSTN) is a data structure for representing and reasoning about time-points and temporal constraints, some of which may apply only in certain scenarios. The scenarios in a CSTN are represented by conjunctions of propositional literals whose truth values are not known in advance, but instead are observed in real time, during execution. The most important property of a CSTN is whether it is dynamically consistent (DC), that is, whether there exists a strategy for executing its time-points such that all relevant constraints are guaranteed to be satisfied no matter which scenario is incrementally revealed during execution. Prior approaches to determining the dynamic consistency of CSTNs (a.k.a., solving the Conditional Simple Temporal Problem) are primarily of theoretical interest, they have not been realized in practical algorithms. This paper presents a sound-and-complete DC-checking algorithm for CSTNs that is based on the propagation of constraints labeled by propositions. The paper also presents an empirical evaluation of the new algorithm that demonstrates that it may be practical for a variety of applications. This is the first empirical evaluation of any DC-checking algorithm for CSTNs ever reported in the literature.

editorial Free Access Share on Introduction to the ACM TIST Special Issue on Intelligent Healthcare Informatics Editors: Carlo Combi View Profile , Jiming Liu View Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 6Issue 4August 2015 Article No.: 51pp 1–3https://doi.org/10.1145/2791398Published:04 July 2015Publication History 0citation216DownloadsMetricsTotal Citations0Total Downloads216Last 12 Months30Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes in several different application domains. Among them, the representation and management of health and clinical processes have been attracting a growing interest. Such processes are in general related to the way each health organization provides the required healthcare services. Health and clinical processes underlie the specification and application of clinical protocols, clinical guidelines, clinical pathways, and the most common clinical/administrative procedures. Current workflow systems are lacking in effective management of three general key aspects that are common (not only) in the clinical/health context: data dependencies, exception handling, and temporal constraints. For example, a laparoscopic intervention may need the results of the concurrent bioptic analysis to be properly concluded while exceptional recovery activities have to be performed in case of emergency evidence during standard treatment; however, the successful application of a fibrinolytic therapy requires a maximum delay of 30 min after the admission into the emergency department. In this paper, we propose TNest, a new advanced, structured, and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design, in addition to exception handling and compensation activities. As for temporal constraints, we focus here on temporal controllability which is the capability of executing a workflow for all possible durations of all tasks satisfying all temporal constraints. Moreover, we analyze the computational complexity of the temporal controllability problem in TNest, and we propose a general algorithm to check the controllability. All the features of TNest that have been considered to model clinical pathways from classical clinical guidelines, i.e., those features for the management of STEMI patients, published by the American College of Cardiology/American Heart Association, will be used throughout the paper as a motivating scenario.

In epidemiology spatio-temporal data may represent surveillance data and origins of diseases. In order to better exploit these data, temporal and spatial dimensions could be managed considering them as meta-data useful to retrieve classical data. In this paper, we propose to use a framework for spatio-temporal granularities with the aim to improve the querying of clinical spatio-temporal data. We show how granularities can be used to enrich a psychiatric case register. We exemplify our approach reporting spatio-temporal queries, based on granularities, useful for epidemiological studies.

The Smart Health and Wellbeing workshop is organized to develop a platform for authors to discuss fundamental principles, algorithms or applications of intelligent data acquisition, processing and analysis of healthcare data. We are particularly interested in information and knowledge management papers, in which the approaches are accompanied by an in-depth experimental evaluation with real world data. This paper provides an overview of the workshop and the accepted contributions.

In Pozzani and Combi proposed ST4SQL, an SQL-based query language extending SQL with new constructs for querying spatio-temporal data. In particular ST4SQL deals with different temporal and spatial semantics, allowing one to specify how the system has to manage temporal and spatial dimensions for evaluating queries. Moreover, the query language introduces new constructs for grouping data with respect to temporal and spatial dimensions. All proposed constructs take into account data qualified with granularities [2]. In this paper we briefly present ST4SQL and we present, also through some examples, its semantics with respect to the standard SQL one.

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes. The management of temporal aspects in the definition of a workflow process has been considered only recently in the literature. 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 article, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and we present a general technique to check different levels of temporal consistency for workflow schemata at process design time: since a time constraint can be satisfied in different ways, we propose a classification of temporal workflows according to the way time constraints are satisfied. Such classification can be used to successfully manage flexible workflows at runtime.

Spatial granularities allow one to qualify classical data adding them space locations. In order to compare data qualified with different granularities and to associate data to different granularities (e.g., in analysis similar to drill-down and roll-up operations), it is necessary to know how the involved granularities are related. However, the explicit evaluation of these relationships may be heavy from a computational point of view. Moreover, the explicit evaluation of these relationships could not be requested, as relationships can be derived from already established ones. Thus, in this paper, we propose an inference system for deriving spatial relationships that definitely hold, starting from a given set of relationships between spatial granularities, without evaluating them explicitly.

In the last years the representation and management of temporal information has become crucial for several computer applications. In the temporal database literature, every fact stored into a database may be equipped with two temporal dimensions: the valid time, that describes the time when the fact is true in the modeled reality, and the transaction time, that describes the time when the fact is current in the database and it can be retrieved. Temporal functional dependencies (TFDs) add (transaction) valid time to classical functional dependencies (FDs) in order to express database integrity constraints over the flow of time. Currently, proposals dealing with TFDs adopt a point-based approach, where tuples hold at specific time points. Moreover, TFDs may involve the use of different granularities (i.e., partitions of the time domain), to express integrity constraints as "for each month, the salary of an employee depends only on his role". At the best of our knowledge, there are no proposals dealing with interval-based temporal functional dependencies (ITFDs for short) where the associated valid time is represented by an interval. In this paper, we propose a set of ITFDs based on the Allen's interval relations, we analyze their expressive power with respect to other TFDs proposed in the literature and we propose an algorithm for verifying ITFDs in a database system.

The modelling and management of temporal constraints over business processes has received some attention in the past years. Recently, we have introduced and discussed the concept of "controllability" for workflow schemata modelling real world business processes: controllability, originally introduced in the AI community for temporal constraint networks, refers to the capability of executing a workflow for all possible durations of all tasks. In this paper, we first extend the execution strategy proposed by Morris, Muscettola and Vidal in the context of temporal constraint networks, to deal with the execution of business processes in a more suitable way. Then, we discuss and propose a new algorithm to deal with the (dynamic) controllability of an overall workflow schema, where several, possibly disjoint, execution paths are possible, due to the presence of alternative paths in the workflow schema. We show that the presence of several controllable alternative execution paths in a workflow schema does not guarantee that the overall workflow schema is controllable.

In the database research field the management of temporal and spatial information is crucial. Many information may be temporally and spatially qualified, i.e., data may be associated to temporal and spatial locations. In the management of qualified data, the notion of "granularity" plays an important role; it allows one to reason about temporal and spatial data expressed with different units of measure.

Logical and algebraic approaches have been proposed in the literature for the definition and the management of time granularities. In the algebraic framework, a bottom granularity is assumed, and new granularities are created by means of suitable algebraic operators. In the logical framework, mathematical structures are used to represent time granularities, and logical formulae are used to specify meaningful properties between them. In this paper we extend the logical approach of Combi et al. for representing and reasoning about temporal granularities, defining logical formulae corresponding to calendar algebra operators. This approach allows one to define a large set of granularities,possibly corresponding to complex formulae, in a natural way. Indeed, it gives the possibility of specifying granularity-related formulae in an easier way through operators that are intuitive and natural as the algebraic ones, still maintaining the capability of reasoning on granularities through the (formal) techniques that have been deeply investigated for the logical framework.

In the spatial context, the research community has not reached yet a widely accepted definition of granularity. There are relatively few papers about "spatial granularities " and many of them give different definitions and often refer to different interpretations of the term "spatial granularity". In this work we propose a formal definition of "spatial granularity" and other related terms involved in the spatial context, as happened for the temporal one. After that, we will merge together our framework for spatial granularity with the usual framework for temporal granularity, giving a definition of spatio-temporal granularity able to manage information about spatial granularities changing over time.

Time characterizes every aspect of our life and its management when storing and querying data is very important. In this paper we propose a new temporal query language, called T4SQL, supporting multiple temporal dimensions of data. Besides the well-known valid and transaction times, it encompasses two additional temporal dimensions, namely, availability and event times. The availability time records when information is known and treated as true by the information system; the event times record the occurrence times of both the event that starts the valid time and the event that ends it. T4SQL is capable to deal with different temporal semantics (atemporal aka non-sequenced, current, sequenced, next) with respect to every temporal dimension. Moreover, T4SQL provides a novel temporal grouping clause and an orthogonal management of temporal properties when defining the selection condition(s) and the schema for the output relation.

The diffusion of clinical guidelines to describe the proper way to deal with patients' situations is spreading out and opens new issues in the context of modeling and managing (temporal) information about medical activities. Guidelines can be seen as processes describing the sequence of activities to be executed, and thus approaches proposed in the business context can be used to model them. In this paper, we propose a general conceptual workflow model, considering both activities and their temporal properties, and focus on the representation of clinical guidelines by the proposed model.

A workflow management system (WfMS) supports the coordinated execution of simple activities (tasks), assigning them to human or automatic executors (agents), to achieve the goal defined for a business process. The workflow scheduler performs task assignment policies: temporal aspects, such as availability time and skills of agents, deadlines for task completion, constraints over timestamps for task activation, periodic constraints cannot be neglected. By this paper we propose a temporal workflow scheduler which pervasively considers all the temporalities affecting task assignment policies

In this paper we propose a formal framework allowing the expression of temporal trends which involve multiple granularities. We first discuss the representation of trend dependencies and then we propose a characterization of trends according to different temporal features considering also the relationships with the specific temporal granularities involved in the trend definition. A suitable temporal logic is introduced to formally represent time-evolving relations, granularities and granular trends

In the database literature, different types of temporal functional dependencies (TFDs) have been proposed to constrain the temporal evolution of information. Unfortunately, the lack of a common notation makes it difficult to compare, to integrate, and to possibly extend the various proposals. In this paper, we outline a unifying algebraic framework for TFDs. We first introduce the proposed approach, then we use it to give a uniform account of existing TFDs, and finally we show that it allows one to easily express new meaningful TFDs.

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.

We propose a logical approach to represent and reason about different time granularities. We identify a time granularity as a discrete infinite sequence of time points properly labelled with proposition symbols marking the starting and ending points of the corresponding granules, and we intensively model sets of granularities with linear time logic formulas. Some real-world granularities are provided to motivate and exemplify our approach. The proposed framework permits 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.

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, also evaluating them with a proper user study.

Granularity is an integral feature of both anchored (e.g., 25 October 1995, July 1996) and unanchored (e.g., 3 minutes, 6 hours 20 minutes, 5 days, 1 week) temporal data. In supporting temporal data that is specified in different granularities, numerous approaches have been proposed to deal with the issues of converting temporal data from one granularity to another. The emphasis, however, has only been on granularity conversions with respect to anchored temporal data. This is because a granularity in these approaches is modeled as an anchored partitioning of the time axis, thereby making it difficult to deal with granularity conversions in unanchored temporal data. In this paper we provide a novel approach to the treatment of granularity in temporal data. A granularity is modeled as a special kind of unanchored temporal primitive that can be used as a unit of time. That is, a granularity is modeled as a unit unanchored temporal primitive. Granularities are accommodated within the cont...

The need of managing temporal information given at different levels of granularity or with indeterminacy is common to many application areas. Among them, we focus on clinical data management. Different time granularities and indeterminacy are also needed in querying temporal databases. In this paper, we describe GCH-OSQL (Granular Clinical History-Object Structured Query Language), an object-oriented temporally-oriented extension of SQL. GCH-OSQL is based on an object-oriented temporal data model, GCH-OODM. GCH-OODM allows storage of clinical information at different and mixed granularities or with temporal indeterminacy. GCH-OSQL deals with the valid time of clinical information. The temporal extension of the SELECT construct includes the addition of the TIME-SLICE and MOVING WINDOW clauses, and the capability to reference the temporal dimension of objects in the WHERE and SELECT clauses. Using object-oriented technologies, a system prototype for GCH-OSQL and GCH-OODM has been implemented and applied to data management of follow-up patients after coronary angioplasty intervention.