User-Centered Event Data Modelling and Analytics

Conventional data analytics platforms are not adequate to be applied in the management of emergency situations. The 3V the usually characterize big data (volume, variety, velocity) along with the issue of integrating information coming from heterogeneous networks require the development of new systems. In this paper we provide the design of a data analytics platform that we are developing around the concept of event, that is simple or complex data stream gathered from physical and social sensors that are encapsulated with contextual information (space, time, thematics). Copyright \ua9 2014 for the individual papers by the papers' authors

Event-Oriented Dynamic Adaptation of Workflows: Model, Architecture and Implementation

Workflow management is widely accepted as a core technology to support long-term business processes in heterogeneous and distributed environments. However, conventional workflow management systems do not provide sufficient flexibility support to cope with the broad range of failure situations that may occur during workflow execution. In particular, most systems do not allow to dynamically adapt a workflow due to a failure situation, e.g., to dynamically drop or insert execution steps. As a contribution to overcome these limitations, this dissertation introduces the agent-based workflow management system AgentWork. AgentWork supports the definition, the execution and, as its main contribution, the event-oriented and semi-automated dynamic adaptation of workflows. Two strategies for automatic workflow adaptation are provided. Predictive adaptation adapts workflow parts affected by a failure in advance (predictively), typically as soon as the failure is detected. This is advantageous in many situations and gives enough time to meet organizational constraints for adapted workflow parts. Reactive adaptation is typically performed when predictive adaptation is not possible. In this case, adaptation is performed when the affected workflow part is to be executed, e.g., before an activity is executed it is checked whether it is subject to a workflow adaptation such as dropping, postponement or replacement. In particular, the following contributions are provided by AgentWork: A Formal Model for Workflow Definition, Execution, and Estimation: In this context, AgentWork first provides an object-oriented workflow definition language. This language allows for the definition of a workflow\u92s control and data flow. Furthermore, a workflow\u92s cooperation with other workflows or workflow systems can be specified. Second, AgentWork provides a precise workflow execution model. This is necessary, as a running workflow usually is a complex collection of concurrent activities and data flow processes, and as failure situations and dynamic adaptations affect running workflows. Furthermore, mechanisms for the estimation of a workflow\u92s future execution behavior are provided. These mechanisms are of particular importance for predictive adaptation. Mechanisms for Determining and Processing Failure Events and Failure Actions: AgentWork provides mechanisms to decide whether an event constitutes a failure situation and what has to be done to cope with this failure. This is formally achieved by evaluating event-condition-action rules where the event-condition part describes under which condition an event has to be viewed as a failure event. The action part represents the necessary actions needed to cope with the failure. To support the temporal dimension of events and actions, this dissertation provides a novel event-condition-action model based on a temporal object-oriented logic. Mechanisms for the Adaptation of Affected Workflows: In case of failure situations it has to be decided how an affected workflow has to be dynamically adapted on the node and edge level. AgentWork provides a novel approach that combines the two principal strategies reactive adaptation and predictive adaptation. Depending on the context of the failure, the appropriate strategy is selected. Furthermore, control flow adaptation operators are provided which translate failure actions into structural control flow adaptations. Data flow operators adapt the data flow after a control flow adaptation, if necessary. Mechanisms for the Handling of Inter-Workflow Implications of Failure Situations: AgentWork provides novel mechanisms to decide whether a failure situation occurring to a workflow affects other workflows that communicate and cooperate with this workflow. In particular, AgentWork derives the temporal implications of a dynamic adaptation by estimating the duration that will be needed to process the changed workflow definition (in comparison with the original definition). Furthermore, qualitative implications of the dynamic change are determined. For this purpose, so-called quality measuring objects are introduced. All mechanisms provided by AgentWork include that users may interact during the failure handling process. In particular, the user has the possibility to reject or modify suggested workflow adaptations. A Prototypical Implementation: Finally, a prototypical Corba-based implementation of AgentWork is described. This implementation supports the integration of AgentWork into the distributed and heterogeneous environments of real-world organizations such as hospitals or insurance business enterprises

Ontology-based Consistent Specification and Scalable Execution of Sensor Data Acquisition Plans in Cross-Domain loT Platforms

Nowadays there is an increased number of vertical Internet of Things (IoT) applications that have been developed within IoT Platforms that often do not interact with each other because of the adoption of different standards and formats. Several efforts are devoted to the construction of software infrastructures that facilitate the interoperability among heterogeneous cross-domain IoT platforms for the realization of horizontal applications. Even if their realization poses different challenges across all layers of the network stack, in this thesis we focus on the interoperability issues that arise at the data management layer. Starting from a flexible multi-granular Spatio-Temporal-Thematic data model according to which events generated by different kinds of sensors can be represented, we propose a Semantic Virtualization approach according to which the sensors belonging to different IoT platforms and the schema of the produced event streams are described in a Domain Ontology, obtained through the extension of the well-known ontologies (SSN and IoT-Lite ontologies) to the needs of a specific domain. Then, these sensors can be exploited for the creation of Data Acquisition Plans (DAPs) by means of which the streams of events can be filtered, merged, and aggregated in a meaningful way. Notions of soundness and consistency are introduced to bind the output streams of the services contained in the DAP with the Domain Ontology for providing a semantic description of its final output. The facilities of the \streamLoader prototype are finally presented for supporting the domain experts in the Semantic Virtualization of the sensors and for the construction of meaningful DAPs. Different graphical facilities have been developed for supporting domain experts in the development of complex DAPs. The system provides also facilities for their syntax-based translations in the Apache Spark Streaming language and execution in real time in a distributed cluster of machines

Handling Expiration of Multigranular Temporal Objects

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both