1,499 research outputs found
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
A Bayesian Approach to Graphical Record Linkage and De-duplication
We propose an unsupervised approach for linking records across arbitrarily
many files, while simultaneously detecting duplicate records within files. Our
key innovation involves the representation of the pattern of links between
records as a bipartite graph, in which records are directly linked to latent
true individuals, and only indirectly linked to other records. This flexible
representation of the linkage structure naturally allows us to estimate the
attributes of the unique observable people in the population, calculate
transitive linkage probabilities across records (and represent this visually),
and propagate the uncertainty of record linkage into later analyses. Our method
makes it particularly easy to integrate record linkage with post-processing
procedures such as logistic regression, capture-recapture, etc. Our linkage
structure lends itself to an efficient, linear-time, hybrid Markov chain Monte
Carlo algorithm, which overcomes many obstacles encountered by previously
record linkage approaches, despite the high-dimensional parameter space. We
illustrate our method using longitudinal data from the National Long Term Care
Survey and with data from the Italian Survey on Household and Wealth, where we
assess the accuracy of our method and show it to be better in terms of error
rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In
press, Journal of the American Statistical Association: Theory and Methods
(2015
Quality and complexity measures for data linkage and deduplication
Summary. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive quality results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity. Key words: data or record linkage, data integration and matching, deduplication, data mining pre-processing, quality and complexity measures
Entity reconciliation in big data sources: A systematic mapping study
The entity reconciliation (ER) problem aroused much interest as a research topic in today’s Big Dataera, full of big and open heterogeneous data sources. This problem poses when relevant information ona topic needs to be obtained using methods based on: (i) identifying records that represent the samereal world entity, and (ii) identifying those records that are similar but do not correspond to the samereal-world entity. ER is an operational intelligence process, whereby organizations can unify differentand heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, thecomplexity that the heterogeneity of data sources involves, the large number of records and differencesamong languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) ofjournal articles, conferences and workshops published from 2010 to 2017 to solve the problem describedbefore, first trying to understand the state-of-the-art, and then identifying any gaps in current research.Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process thathas resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim tosolve ER. The conclusion obtained is that most of the research is based on the operational phase asopposed to the design phase, and most studies have been tested on real-world data sources, where a lotof them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniquesbased on clustering/blocking and graphs, although the level of automation of the proposals is hardly evermentioned in the research work.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED
RefConcile – automated online reconciliation of bibliographic references
Comprehensive bibliographies often rely on community contributions. In such a setting, de-duplication is mandatory for the bibliography to be useful. Ideally, it works online, i.e., during the addition of new references, so the bibliography remains duplicate-free at all times. While de-duplication is well researched, generic approaches do not achieve the result quality required for automated reconciliation. To overcome this problem, we propose a new duplicate detection and reconciliation technique called RefConcile. Aimed specifically at bibliographic references, it uses dedicated blocking and matching techniques tailored to this type of data. Our evaluation based on a large real-world collection of bibliographic references shows that RefConcile scales well, and that it detects and reconciles duplicates highly accurately
End-to-End Entity Resolution for Big Data: A Survey
One of the most important tasks for improving data quality and the
reliability of data analytics results is Entity Resolution (ER). ER aims to
identify different descriptions that refer to the same real-world entity, and
remains a challenging problem. While previous works have studied specific
aspects of ER (and mostly in traditional settings), in this survey, we provide
for the first time an end-to-end view of modern ER workflows, and of the novel
aspects of entity indexing and matching methods in order to cope with more than
one of the Big Data characteristics simultaneously. We present the basic
concepts, processing steps and execution strategies that have been proposed by
different communities, i.e., database, semantic Web and machine learning, in
order to cope with the loose structuredness, extreme diversity, high speed and
large scale of entity descriptions used by real-world applications. Finally, we
provide a synthetic discussion of the existing approaches, and conclude with a
detailed presentation of open research directions
A survey on Data Extraction and Data Duplication Detection
Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Processing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algorithms are needed to extract useful features from huge amount of data. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. This Paper review the literature on duplicate detection and data fusion (remov e and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user
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