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.

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.

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.

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.

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