Entity Resolution in the Web of Data

VassilisChristophides,; Efthymiou, Vasilis; KostasStefanidis,

doi:10.2200/s00655ed1v01y201507wbe013

Cited by 64 publications

(56 citation statements)

References 149 publications

(176 reference statements)

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“…Entity resolution [76,77] is the process of identifying and merging references corresponding to the same real world entity. An entity could be a specific person, place, building, etc.…”

Section: Personal Data Matchingmentioning

confidence: 99%

Privacy challenges in Ambient Intelligence systems

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Moawad

Efthymiou

et al. 2016

AIS

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Abstract. Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the "right to be left alone" to being a "commodity" that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The humancentred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

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“…Entity resolution [76,77] is the process of identifying and merging references corresponding to the same real world entity. An entity could be a specific person, place, building, etc.…”

Section: Personal Data Matchingmentioning

confidence: 99%

Privacy challenges in Ambient Intelligence systems

Caire

Moawad

Efthymiou

et al. 2016

AIS

Self Cite

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“…ER constitutes a well-studied problem [7], [6], [9]. Due to its quadratic complexity, a bulk of work aims at improving its scalability.…”

Section: Related Workmentioning

confidence: 99%

“…Entity resolution (ER) is the task of mapping different entities to the same real-world object [6]. In the context of Big Web Data, it constitutes a batch process of quadratic complexity that is confronted with two Vs: volume, as it receives a large number of profiles as input, and variety, because the profiles are described by diverse schemata [16], [17] (velocity appears in Incremental ER).…”

Section: Introductionmentioning

confidence: 99%

Parallel meta-blocking: Realizing scalable entity resolution over large, heterogeneous data

Efthymiou

Papadakis

Papastefanatos

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. Typically, it scales to large volumes of data through blocking: similar entities are clustered into blocks so that it suffices to perform comparisons only within each block. Meta-blocking further increases efficiency by cleaning the overlapping blocks from unnecessary comparisons. However, even Meta-blocking can be time-consuming: applying it to blocks with 7.4 million entities and 2.2·10 11 comparisons takes almost 8 days on a modern high-end server. In this paper, we parallelize Metablocking based on MapReduce. We propose a simple strategy that explicitly creates the core concept of Meta-blocking, the blocking graph. We then describe an advanced strategy that creates the blocking graph implicitly, reducing the overhead of data exchange. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the superiority of our advanced strategy and demonstrates an almost linear speedup for all meta-blocking techniques with respect to the number of available nodes.

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“…This can be viewed intuitively as a measure of information that X and Y are sharing. In particular, it measures how much knowing one of these variables reduces uncertainty about the other [7][8][9]. In [9], examples are explaining this quantity.…”

Section: Mutual Informationmentioning

confidence: 99%