Relational clustering for multi-type entity resolution

Bhattacharya, Indrajit; Getoor, Lise

doi:10.1145/1090193.1090195

Cited by 44 publications

(96 citation statements)

References 18 publications

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“…It is a crucial task in data cleaning, and it has applies in many scenarios such as data integration, customer relationship management, and personal information management [1]. The duplicate problem has been studied extensively under various names, such as merge/purge [2], record linkage [3], entity resolution [4], or reference reconciliation [5], too names but a few [6]. There are some researches on real-time de-duplication recently [7].…”

Section: Related Workmentioning

confidence: 99%

De-Duplication Scheduling Strategy in Real-Time Data Warehouse

Song¹,

Liu²,

Wu³

et al. 2015

TOCSJ

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Data quality of the data warehouse is crucial to decision-makers. Data duplication is considered one of the critical factors that affect the data quality. Therefore, data de-duplication is an essential process for data warehousing. Particularly, for a real-time data warehouse, it is necessary to ensure not only the data quality in real-time, but also the performance of the front-end queries and analysis. The scheduling strategy of de-duplication in a real-time data warehouse should be well studied. In this paper, we firstly investigate the three kinds of data de-duplication scheduling strategies named De-duplication Prior scheduling Strategy (DPS), Real-time scheduling Strategy (RS) and ETL Prior scheduling Strategy (EPS); then propose a new Time-Triggered scheduling Strategy (TTS) which belongs to EPS; finally evaluate the performance of the proposed scheduling strategy through experiments. This work is contributed to the efficient data cleaning and application of real-time data warehouse.

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Section: Related Workmentioning

confidence: 99%

De-Duplication Scheduling Strategy in Real-Time Data Warehouse

Song¹,

Liu²,

Wu³

et al. 2015

TOCSJ

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“…However, when working with relational data there are more types of information available that need to be used to distinguish groupings [12]. Clustering in multi-relational data has been studied in some works [2,12,13]. When computing similarity between two instances, they have to consider not only the attributes vales, but also the inter relationship between the instances.…”

Section: Related Workmentioning

confidence: 99%

“…As similarity measure plays an important role on clustering different objects (instances), some measures have been proposed by researches [5]. In addition, other works have been produced to show the results of clustering only attribute information and both attribute and inter relationships information [2,12].…”

Section: Related Workmentioning

confidence: 99%

Web log data clustering for a multi-agent recommendation system

Xavier-Júnior

Freitas

Canuto

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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Abstract:In this paper, we propose an automatic way of recommending information to be visualized by users. The list of information to be recommended is generated based on the web logs of the users stored by the system in a multi relational database. This system is a web-based multi-agent system which provides geographical information and monitors the actions of the users by generating logs. Frequently, the system joins the relational web log data and runs a clustering algorithm in order to recommend a list of most accessed information up to that moment to registered users who log in the system.

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“…Such approaches are called feature-based similarity (FBS) techniques [12,13,26]. Recently, a number of techniques have been proposed that go beyond the traditional approach [1,3,8,10,[16][17][18][19]23,27,30]. Ananthakrishna et al [1] presented relational deduplication in data warehouses where there is dimensional hierarchy over the relations.…”

Section: Disambiguationmentioning

confidence: 99%

“…Ananthakrishna et al [1] presented relational deduplication in data warehouses where there is dimensional hierarchy over the relations. Bhattacharya and Getoor introduced a method which requires that social groups function as cliques [3]. This model expects that there are strong correlations between pairs or sets of entities, such that they often co-occur in information sources.…”

Section: Disambiguationmentioning

confidence: 99%

Self-tuning in Graph-Based Reference Disambiguation

Nuray-Turan

Kalashnikov

Mehrotra

Advances in Databases: Concepts, Systems and Applications

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Abstract. Nowadays many data mining/analysis applications use the graph analysis techniques for decision making. Many of these techniques are based on the importance of relationships among the interacting units. A number of models and measures that analyze the relationship importance (link structure) have been proposed (e.g., centrality, importance and page rank) and they are generally based on intuition, where the analyst intuitively decides a reasonable model that fits the underlying data. In this paper, we address the problem of learning such models directly from training data. Specifically, we study a way to calibrate a connection strength measure from training data in the context of reference disambiguation problem. Experimental evaluation demonstrates that the proposed model surpasses the best model used for reference disambiguation in the past, leading to better quality of reference disambiguation.

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Relational clustering for multi-type entity resolution

Cited by 44 publications

References 18 publications

De-Duplication Scheduling Strategy in Real-Time Data Warehouse

De-Duplication Scheduling Strategy in Real-Time Data Warehouse

Web log data clustering for a multi-agent recommendation system

Self-tuning in Graph-Based Reference Disambiguation

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