18,406 research outputs found
Clustering Uncertain Data with Possible Worlds
The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data
Indeterministic Handling of Uncertain Decisions in Duplicate Detection
In current research, duplicate detection is usually considered as a deterministic approach in which tuples are either declared as duplicates or not. However, most often it is not completely clear whether two tuples represent the same real-world entity or not. In deterministic approaches, however, this uncertainty is ignored, which in turn can lead to false decisions. In this paper, we present an indeterministic approach for handling uncertain decisions in a duplicate detection process by using a probabilistic target schema. Thus, instead of deciding between multiple possible worlds, all these worlds can be modeled in the resulting data. This approach minimizes the negative impacts of false decisions. Furthermore, the duplicate detection process becomes almost fully automatic and human effort can be reduced to a large extent. Unfortunately, a full-indeterministic approach is by definition too expensive (in time as well as in storage) and hence impractical. For that reason, we additionally introduce several semi-indeterministic methods for heuristically reducing the set of indeterministic handled decisions in a meaningful way
Injecting Uncertainty in Graphs for Identity Obfuscation
Data collected nowadays by social-networking applications create fascinating
opportunities for building novel services, as well as expanding our
understanding about social structures and their dynamics. Unfortunately,
publishing social-network graphs is considered an ill-advised practice due to
privacy concerns. To alleviate this problem, several anonymization methods have
been proposed, aiming at reducing the risk of a privacy breach on the published
data, while still allowing to analyze them and draw relevant conclusions. In
this paper we introduce a new anonymization approach that is based on injecting
uncertainty in social graphs and publishing the resulting uncertain graphs.
While existing approaches obfuscate graph data by adding or removing edges
entirely, we propose using a finer-grained perturbation that adds or removes
edges partially: this way we can achieve the same desired level of obfuscation
with smaller changes in the data, thus maintaining higher utility. Our
experiments on real-world networks confirm that at the same level of identity
obfuscation our method provides higher usefulness than existing randomized
methods that publish standard graphs.Comment: VLDB201
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
Qualitative Effects of Knowledge Rules in Probabilistic Data Integration
One of the problems in data integration is data overlap: the fact that different data sources have data on the same real world entities. Much development time in data integration projects is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates from the integration result or solve other semantic conflicts, but it proofs impossible to get rid of all semantic problems in data integration. An often-used rule of thumb states that about 90% of the development effort is devoted to solving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that stores any remaining semantic uncertainty and conflicts in a probabilistic database enabling it to already be meaningfully used. The main development effort in our approach is devoted to defining and tuning knowledge rules and thresholds. Rules and thresholds directly impact the size and quality of the integration result. We measure integration quality indirectly by measuring the quality of answers to queries on the integrated data set in an information retrieval-like way. The main contribution of this report is an experimental investigation of the effects and sensitivity of rule definition and threshold tuning on the integration quality. This proves that our approach indeed reduces development effort — and not merely shifts the effort to rule definition and threshold tuning — by showing that setting rough safe thresholds and defining only a few rules suffices to produce a ‘good enough’ integration that can be meaningfully used
Anonymizing Social Graphs via Uncertainty Semantics
Rather than anonymizing social graphs by generalizing them to super
nodes/edges or adding/removing nodes and edges to satisfy given privacy
parameters, recent methods exploit the semantics of uncertain graphs to achieve
privacy protection of participating entities and their relationship. These
techniques anonymize a deterministic graph by converting it into an uncertain
form. In this paper, we propose a generalized obfuscation model based on
uncertain adjacency matrices that keep expected node degrees equal to those in
the unanonymized graph. We analyze two recently proposed schemes and show their
fitting into the model. We also point out disadvantages in each method and
present several elegant techniques to fill the gap between them. Finally, to
support fair comparisons, we develop a new tradeoff quantifying framework by
leveraging the concept of incorrectness in location privacy research.
Experiments on large social graphs demonstrate the effectiveness of our
schemes
Integration of Biological Sources: Exploring the Case of Protein Homology
Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration
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