Daniela Grigori

Interaction System based on Internet of Things as Support for Education

Abstract The Internet of Things is a new paradigm that is revolutionizing computing. It is intended that all objects around us are connected to the network, providing “anytime, anywhere” access to information. This concept is gaining ground, thanks to advances in nanotechnology which allows the creation of devices capable of connecting to the Internet efficiently. Nowdays a large number of devices are connected to the web, ranging from mobile devices to appliances. In this paper we focus on the education field, where Internet of Things can be used to create more significant learning spaces. In this sense, we propose a system that allows students to interact with physical surrounding objects which are virtualy associated with a subject of learning. We conduct an experimental validation of our approach, yielding evidence that our model improves the students learning outcomes.

A Graph-Based Approach for Semantic Process Model Discovery

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.

Discovering Characteristics that Affect Process Control Flow

In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases’ characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.

A Bipolar Approach to the Handling of User Preferences in Business Processes Retrieval

In the context of retrieving business processes, most approaches still lead to a high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. As these services may offer different Quality of Services (QoS) values, taking into account user preferences on QoS attributes allow to retrieve the most appropriate services. In this paper, we advocate an approach for handling preferences on QoS parameters in a bipolar way, i.e., distinguishing between negative and positive preferences as it seems to be the case in the human mind. Negative preferences represent constraints that have to be satisfied by the returned services, while positive preferences express wishes and are not compulsory. Finally, a set of experiments based on real data is conducted to demonstrate the relevance and the scalability of our proposal.

Business Process Paradigms

This chapter provides an overview of the technological landscape surrounding business process management and sets the stage for understanding the different aspects of analyzing business processes with the aim of improving them. The goal of this chapter is to develop an advanced recognition of the potential gaps and thereby an appreciation for key areas of improvement needed to target successful future growth in process analytics. After presenting an overview of the quintessential facets/dimensions often used to describe process types, the chapter examines the various identified implementation technologies and surveys the relevant support tools categorized according to process paradigm.

Spectral Graph Approach for Process Model Matchmaking

In this paper, we propose a novel approach for graph based web service matching. Our approach is inspired by spectral graph matching methods, in particular, by eigen-based projections. We introduce new mechanisms to perform the matchmaking at both structural and semantic levels. These mechanisms are based on algebraic graph techniques that make them run fast and thus suitable for large scale web service matching problems. Experimentation is provided to show the performance of the proposed approach.

A framework for service discovery based on structural similarity and quality satisfaction

The increasing number of published web services rendered the searching for a service within repositories a critical issue in many application domains. Recent approaches resorted to service structure and to preferences over quality attributes to reduce selectivity rate. In this paper, we present S-MatchMaker, a tool for service discovery based on both service structure and quality preferences. The tool implements several algorithms that can be coupled in different ways to provide a personalized solution for service discovery.

Adding non-functional preferences to service discovery

The growth of the number of published services rendered searching for a specific service within repositories a critical issue. In this paper, we present an approach to extend structure-based service discovery by making it sensitive to user preferences over service quality defined at different granularity levels of the service structure.

Keyword-Based Search of Workflow Fragments and Their Composition

Workflow specification, in science as in business, can be a difficult task, since it requires a deep knowledge of the domain to be able to model the chaining of the steps that compose the process of interest, as well as awareness of the computational tools, e.g., services, that can be utilized to enact such steps. To assist designers in this task, we investigate in this paper a methodology that consists in exploiting existing workflow specifications that are stored and shared in repositories, to identify workflow fragments that can be re-utilized and re-purposed by designers when specifying new workflows. Specifically, we present a method for identifying fragments that are frequently used across workflows in existing repositories, and therefore are likely to incarnate patterns that can be reused in new workflows. We present a keyword-based search method for identifying the fragments that are relevant for the needs of a given workflow designer. We go on to present an algorithm for composing the retrieved fragments with the initial (incomplete) workflow that the user designed, based on compatibility rules that we identified, and showcase how the algorithm operates using an example from eScience.

Mining Workflow Repositories for Improving Fragments Reuse

Public repositories of scientific and business workflows are gaining growing attention as a means to enable understanding, reuse and ultimately the reproducibility of the processes such workflows incarnate. However, as the number of workflows hosted by such repositories grows, their users face difficulties when it come to exploring and querying workflows. In this paper, we explore a functionality that can help repository administrators to index their workflows, and users to identify the workflows that are of interest to them. In particular, we investigate the problem of finding frequent and similar fragments in workflows using graph mining techniques. Our objective is not to come up with yet another graph mining or similarity technique. Instead, we explore different representations that can be used for encoding workflows before assessing their similarity taking into consideration the effectiveness and efficiency of the mining algorithm.