Process mining techniques allow organizations to discover, monitor and improve their as-is processes by analyzing the process execution data, aka event data, recorded by their information systems. A recurrent task in process mining is querying. Querying allows users to get insights into specific executions of their processes and to retrieve relevant data. Existing process querying techniques require end users to be knowledgeable of the query language and the database schema. However, a key success factor for process analysis is to make querying accessible to business experts who may be inexperienced in database querying. This paper addresses this challenge by proposing a natural language interface (NLI) for querying event data. The interface allows users to formulate their questions in natural language and to automatically translate the questions into a structured query that can be executed over a database. We use graph based storage techniques, namely labeled property graphs, which allow to explicitly model event data relationships. As an executable query language, we use the Cypher language which is widely used for querying property graphs. The approach has been implemented and evaluated using a publicly available event log.

Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word 2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.

The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media. To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook. Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts. We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas. We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community. The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: relational and NoSQL (i.e., document, graph, key-value, and column) data stores. Interacting with heterogeneous data models via different APIs and query languages imposes challenging tasks to the developers of multiple data stores applications. Indeed, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with single data store application, and therefore requires extra implementation efforts. In this paper we propose a mediation based component to optimize and execute complex queries over multiple data stores in Cloud environments. This component is referred to as virtual data store (VDS). The key ingredients of our solution are (1) a simple global schema describing the different data sources and their relationships, (2) a cost model to evaluate the cost of the operations, (3) an inter data stores parallelism execution model, and (4) a dynamic programming based approach to generate optimal execution plan. Quantitative and qualitative experiments are conducted to validate our approach.

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. In this paper we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unifying data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. Based on that, they express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant cloud environment and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.

International audience

Recently, Sensor/Actuator Networks have become an emergent technology for various application areas such as security and surveillance applications, traffic control, logistics, energy control in public and private buildings, etc. Designing and constructing new applications using these technologies remain, however, a challenging task. Indeed, finding the relevant sensors and actuators, and combining them in a proper way in order to achieve a specific goal is not an easy task and requires several skills from different stakeholders. Moreover, sensor environments are inherently highly dynamic. Furthermore, current applications are in general tightly coupled to the underlying infrastructure which hampers their reuse and flexibility to changes. In this paper, we present a process-oriented and service-based approach for supporting the development of adaptive sensor-based applications. Our approach decouples application logic from its implementation. A design-time model is first specified, as a flow of activities, which is then deployed in a particular environment. Decoupling the application logic from its implementation enables, on one hand, to foster the reuse at the application level and, on the other hand, to adapt the same application to different environments and situations. We propose also an activity recommendation technique to provide assistance to application designers by recommending to them activities that have been used in a similar compositional context. Our approach has been prototyped using service standards such as BPMN within the context of the VITRO European project and validated by several use cases.

Summary The last few years have seen an increase in the use of e‐services as a natural consequence of the service‐based economy growth. However, the distributed nature of services can raise several heterogeneity problems that can hamper the widespread adoption of service‐oriented architectures (SOA). In this paper, we are interested in data heterogeneity problems that can occur when using composite services. We propose a service‐based approach for automatically inserting appropriate mediation services in service compositions to resolve structural heterogeneities in their data flow. This mediation is ensured through a special kind of Web services called data mapping Web services. In addition, in order to be in agreement with the distributed nature of the Web, we propose a decentralized solution to publish/discover these services. We demonstrate how our approach applies to the data structural heterogeneity problem in data providing service compositions as a particular data heterogeneity case. Provided experimental results show that our approach is efficient in realistic situations. Copyright © 2013 John Wiley & Sons, Ltd.

In service-oriented computing, a user usually needs to locate a desired service for: (i) fulfilling her requirements or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the Internet, locating a service online may not be appropriate from the performance perspective, especially in large Internet-based service repositories. Instead, services usually need to be clustered according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper, we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. In addition, we introduce some operations for managing service clusters, when new services emerge or existing services disappear or become unavailable. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly. We have prototyped our approach and the source code is freely available on the web. We have evaluated our clustering approach in different settings and the results are very promising.

Provenance is a key metadata for assessing electronic documents trustworthiness. It gives an indicator on the reliability and the quality of the document content. Most of the applications exchanging and processing documents on the web or in clouds become provenance aware and provides heterogeneous, decentralized and not interoperable provenance data. Most of provenance management systems are either dedicated to a specific application (workflow, database) or a specific data type. Those systems were not conceived to support provenance over distributed and open sources. Hence, collecting and querying provenance from heterogeneous sources is always a challenging task. This work presents a new provenance management framework based on semantic web technologies. It allows to import provenance sources, to enrich them semantically to obtain high level representation of provenance. It supports semantic correlation between different provenance sources and allows the use of a high level semantic query language.

Today, most of the applications exchanging and processing documents on the web or in clouds become provenance aware and provides heterogeneous, decentralized and not interoperable provenance data. Provenance is becoming a key metadata for assessing electronic documents trustworthiness and should be considered as first class data.

Today, thanks to vehicular networks, drivers may receive useful information produced or relayed by neighboring sensors or vehicles (e.g., the location of an available parking space, of a traffic congestion, etc.). In this paper, we address the problem of providing assistance to the driver when no recent information has been received on his/her vehicle. Therefore, we present a cooperative scheme to aggregate, store and exchange these events in order to have an history of past events. This scheme is based on a dedicated spatio-temporal aggregation structure using Flajolet-Martin sketches and deployed on each vehicle. Contrary to existing approaches considering data aggregation in vehicular networks, our main goal here is not to save network bandwidth but rather to extract useful knowledge from previous observations. In this paper, we present our aggregation data structure, the associated exchange protocol and a set of experiments showing the effectiveness of our proposal.

Usually semantic metadata are introduced to support effective search of relevant learning objects from LOR (Learning Object Repositories). We propose to use them to assist authors of SCORM (Sharable Content Object Reference Model) like learning objects. In fact, when we design new learning objects by reuse of existing ones, there are risks to have weaknesses mainly due to a bad understanding of the true nature of the reused objects. To overcome those risks we have defined an automated authoring assistance approach. In this paper we focus especially on how we use semantic metadata to produce indicators about the content of learning objects, more precisely about their complexity, heterogeneity and imbalance from a semantic perspective. To attend this specific objective we introduce the notion of "semantic space" and "semantic component". Then we use them to compute metrics which are aggregated together to produce meaningful indicators.

Semantic metadata are not yet fully integrated in most learning metadata profiles. Moreover, when it is supported it is only used to improve the quality of results returned by search engines in LOR (learning object repositories) and LMS (Learning Management Systems). Within the framework of APOGE project, we want to demonstrate the usability of semantic metadata, when they are provided, during the authoring phase to assist authors to improve the consistency of new designed learning objects. The added value of our approach is maximized in the case of SCORM (Sharable Content Object Reference Model) like learning objects' authoring. Indeed, when we have learning objects designed by reuse of existing ones there is risks to have weaknesses mainly due to a bad understanding of the true nature of the reused objects. To overcome those risks we have defined an automated authoring assistance approach. In this paper we put the emphasis on two semantic based methods used in our approach: semantic consistency checking and learning object's semantic space analysis.

The selection of learning objects according with the student profile and the reutilization of existing objects, represents an important element in the quality of learning process. This paper presents a proposal to facilitate the analysis and improvement of SCORM learning objects. Our approach is based on the pedagogic dimension represented by the learning styles and teaching strategies. We describe an authoring tool to help authors with the analysis of SCORM learning objects and propose adapted scenarios to each learning style.

We present a new approach called authoring by reuse to assist author during the design of SCORM like learning objects. It is now easy to design new learning objects by reusing existing ones connected together with dedicated operators. But the results are rarely satisfactory mainly due to their lack of consistency. In this paper we propose an assistance approach (i) providing indicators about the new designed learning object organized on three dimensions (complexity, reusability and pedagogy), (ii) computing metadata entries based on the information available about the reused learning objects and (iii) applying a rule based checking to ensure consistency.

In this paper, we discuss the suitability of using recommendation techniques for Web service discovery in distributed registry environments. The architecture we adopt consists in structuring registries of Web services into groups. We propose to adapt existing techniques for recommendation to ease the services discovery process. This consists of (1) recommendation of groups of registries using semantic matching and (2) recommendation of registries within these groups using user-characterization based technique. To put our proposed distributed registry architecture in practise and to test the efficiency of recommendation-based discovery, we propose to simulate a peer-to-peer (P2P) registries environment on top of JXTA platform.

International audience

The traditional learning style theory is being used in the development of e-learning materials progressively. Usually the process of working with learning styles begins with the development of tests or questionnaires that are focus on determining the preferences of students to learn, and then prepare the material according to them. But once the content is implemented there is no tool to measure or estimate if it complies with the desired learning styles. We use a different approach and in this paper we perform the evaluation of existing pedagogical materials in order to determine the dominant learning style theory if there is any. Then the experimental result shows the classification of all the contents.

Mobile technology provides many benefits to education. In this paper we describe the architecture of a system that allows people to search and retrieve information about its location and activities in a Campus, knowing about the resources available on its surroundings, including buildings, classes, events and people.

VESPA (Vehicular Event Sharing with a mobile P2P Architecture) 1 is a system for enabling vehicles to share information in vehicular ad-hoc networks (VANETs). The originality of VESPA is to process and disseminate any type of event (e.g., available parking spaces, accidents, emergency braking, infor

There are lots of learning objects stored on numerous repositories. They are described by metadata allowing users (either authors or learners) to search and reuse these learning objects. The process of reuse is not so simple and requires some attention during the design phase. In our opinion, it seems clear that semantic metadata are required to allow a real reusing and assembling of learning objects. Following this principle we have defined a system based on three semantic models used to describe the domain, learners and learning objects. In this paper we investigate how to improve the design process of resources described using our proposal. For this, we propose two kinds of approaches: metrics and types. Using metrics, authors may have information about several aspects of quality of their existing resources. Types allow constraining the authoring process by defining constraints to be enforced by new learning objects.

Our claim is that semantic metadata are required to allow a real reusing and assembling of learning objects. Our system is based on three models used to describe the knowledge domain, users and learning objects. In order to automatically process these models and make available basic reasoning capabilities, we have defined mappings to RDF. A prototype is being developed using Sesame, an RDF tool that supports the declarative query language SeRQL. This paper focuses on RDF mappings definition and how to use SeRQL to achieve the functionality required by our approach.

This paper presents a method for service-component testing and a suitable CORBA test architecture. This test environment allows the service validation from its components and is a close step towards the execution of the obtained tests on a CORBA environment. Two aspects are relevant: the test of components and the test architecture. The testing method for components is new: it is based on the generation of partial graphs and avoids the combinatorial explosion of number of states of the global system. The test architecture on a CORBA platform is also new, since there are few works on testing based on these environments. As an application we present a case study on a real conference call service.

We describe ESTRELLA a multimedia object oriented data model developed by MATRA. This model is based upon objects, classes (organized in a lattice) and functions (allow to dynamically implement operations on data and new data types). The valid states of the data base are described by a set of integrity constraints. We propose a document model capable to manage structured documents and to index them with a superimposed codes method. We present as well the associated data manipulation language with a navigational interface and content search operators

IOTA is a prototype of an Information Retrieval System which can manage a corpus made of highly structured, full text documents. The first version presented here has intelligent capabilities related to heuristic pattern matching procedures for processing natural language queries, which involve an automatically built thesaurus. The paper emphazises the overall principles of query processing and gives hints about the underlying techniques used while constructing the thesaurus and automatically indexing highly structured documents.

Article Different levels of expertise for an expert system in information retrieval Share on Author: B. Defude Groupe "Systèmes Intelligents de Recherche, d'Informations", Laboratoire Génie Informatique, Institut IMAG Grenoble, BP 68 38402, St Martin D'Heres, Cedex, France Groupe "Systèmes Intelligents de Recherche, d'Informations", Laboratoire Génie Informatique, Institut IMAG Grenoble, BP 68 38402, St Martin D'Heres, Cedex, FranceView Profile Authors Info & Claims SIGIR '85: Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrievalJune 1985 Pages 147–153https://doi.org/10.1145/253495.253520Online:05 June 1985Publication History 7citation248DownloadsMetricsTotal Citations7Total Downloads248Last 12 Months2Last 6 weeks0 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access