Report on the First VLDB Workshop on Management of Uncertain Data (MUD 2007)

On Monday September 24th, we organized the first international VLDB workshop on Management of Uncertain Data [dKvKD07]. The idea of this workshop arose a year earlier at the Twente Data Management Workshop on Uncertainty in Databases [dKvK06]. The TDM is a bi-annual workshop organized by the Database group of the University of Twente, for which each time a different topic is chosen. The participants of TDM 2006 were enthusiastic about the topic "Uncertainty in Databases" and strongly expressed the wish for a follow-up co-located with an international conference. To fulfill this wish, we organized the MUD-workshop at VLDB

Handling uncertainty in information extraction

This position paper proposes an interactive approach for developing information extractors based on the ontology definition process with knowledge about possible (in)correctness of annotations. We discuss the problem of managing and manipulating probabilistic dependencies

Duplicate Detection in Probabilistic Data

Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused on the integration of certain source data (relational or XML). There is no work on the integration of uncertain (esp. probabilistic) source data so far. In this paper, we present a first step towards a concise consolidation of probabilistic data. We focus on duplicate detection as a representative and essential step in an integration process. We present techniques for identifying multiple probabilistic representations of the same real-world entities. Furthermore, for increasing the efficiency of the duplicate detection process we introduce search space reduction methods adapted to probabilistic data

Uncertainty handling in named entity extraction and disambiguation for informal text

Social media content represents a large portion of all textual content appearing on the Internet. These streams of user generated content (UGC) provide an opportunity and challenge for media analysts to analyze huge amount of new data and use them to infer and reason with new information. A main challenge of natural language is its ambiguity and vagueness. To automatically resolve ambiguity, the grammatical structure of sentences is used. However, when we move to informal language widely used in social media, the language becomes more ambiguous and thus more challenging for automatic understanding.\ud Information Extraction (IE) is the research field that enables the use of unstructured text in a structured way. Named Entity Extraction (NEE) is a sub task of IE that aims to locate phrases (mentions) in the text that represent names of entities such as persons, organizations or locations regardless of their type. Named Entity Disambiguation (NED) is the task of determining which correct person, place, event, etc. is referred to by a mention.\ud The goal of this paper is to provide an overview on some approaches that mimic the human way of recognition and disambiguation of named entities especially for domains that lack formal sentence structure. The proposed methods open the doors for more sophisticated applications based on users’ contributions on social media. We propose a robust combined framework for NEE and NED in semi-formal and informal text. The achieved robustness has been proven to be valid across languages and domains and to be independent of the selected extraction and disambiguation techniques. It is also shown to be robust against the informality of the used language. We have discovered a reinforcement effect and exploited it a technique that improves extraction quality by feeding back disambiguation results. We present a method of handling the uncertainty involved in extraction to improve the disambiguation results

Analysis of the NIST database towards the composition of vulnerabilities in attack scenarios

The composition of vulnerabilities in attack scenarios has been traditionally performed based on detailed pre- and post-conditions. Although very precise, this approach is dependent on human analysis, is time consuming, and not at all scalable. We investigate the NIST National Vulnerability Database (NVD) with three goals: (i) understand the associations among vulnerability attributes related to impact, exploitability, privilege, type of vulnerability and clues derived from plaintext descriptions, (ii) validate our initial composition model which is based on required access and resulting effect, and (iii) investigate the maturity of XML database technology for performing statistical analyses like this directly on the XML data. In this report, we analyse 27,273 vulnerability entries (CVE 1) from the NVD. Using only nominal information, we are able to e.g. identify clusters in the class of vulnerabilities with no privilege which represent 52% of the entries

Real-Time Data Analytics in Sensor Networks

Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series

Evidence combination for incremental decision-making processes

The establishment of a medical diagnosis is an incremental process highly fraught with uncertainty. At each step of this painstaking process, it may be beneficial to be able to quantify the uncertainty linked to the diagnosis and steadily update the uncertainty estimation using available sources of information, for example user feedback, as they become available. Using the example of medical data in general and EEG data in particular, we show what types of evidence can affect discrete variables such as a medical diagnosis and build a simple and computationally efficient evidence combination model based on the Dempster-Shafer theory