Data cleaning in microsoft SQL server 2005

2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

et al. 2011

Abstract-Smart Video Surveillance (SVS) applications enhance situational awareness by allowing domain analysts to focus on the events of higher priority. This in turn leads to improved decision making, allows for better resource management, and helps to reduce information overload. SVS approaches operate by trying to extract and interpret higher "semantic" level events that occur in video. On of the key challenges of Smart Video Surveillance is that of person identification where the task is for each subject that occur in a video shot to identify the person it corresponds to. The problem of person identification is very complex in the resource constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high quality data. In this paper we connect the problem of person identification in video data with the problem of entity resolution that is common in textual data. Specifically, we show how the PI problem can be successfully resolved using a graph-based entity resolution framework called RelDC that leverages relationships among various entities for disambiguation. We apply the proposed solution to a dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even with low quality video data.

“…, r m }, such that each object is represented by one or more descriptions. Entity Resolution has two main instances: Lookup [2], [5] and Grouping [2], [9].…”

Section: A Er Problem Definitionmentioning

confidence: 99%

mentioning

confidence: 99%

Video entity resolution: Applying ER techniques for Smart Video Surveillance

Zhang

Vaisenberg

2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

et al. 2011

“…For example, entity resolution is known as fuzzy grouping operation in the data-integration module of Microsoft SQL Server DBMS [10]. Having a less analystdependent technique makes that operation of wide applicability, so that non-expert users can apply it to their datasets.…”

Section: Motivation For Adaptivenessmentioning

confidence: 99%

“…This is a more general disambiguation challenge known as entity resolution. It is also known as fuzzy grouping [10] and object consolidation [2]. In the generic settings of this disambiguation problem, a dataset D describes a set of entities E = {e1, e2, .…”

Section: Introductionmentioning

confidence: 99%

Adaptive graphical approach to entity resolution

Chen

Kalashnikov

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

2007

Entity resolution is a very common Information Quality (IQ) problem with many different applications. In digital libraries, it is related to problems of citation matching and author name disambiguation; in Natural Language Processing, it is related to coreference matching and object identity; in Web application, it is related to Web page disambiguation. The problem of Entity Resolution arises because objects/entities in real world datasets are often referred to by descriptions, which might not be unique identifiers of these entities, leading to ambiguity. The goal is to group all the entity descriptions that refer to the same real world entities. In this paper we present a graphical approach for entity resolution. It complements the traditional methodology with the analysis of the entity-relationship graph constructed for the dataset being analyzed. The paper demonstrates that a technique that measures the degree of interconnectedness between various pairs of nodes in the graph can significantly improve the quality of entity resolution. Furthermore, the paper presents an algorithm for making that technique self-adaptive to the underlying data, thus minimizing the required participation from the domain-analyst and potentially further improving the disambiguation quality.

“…Notice, since FBS does not use any relationships, including the random noise, its curve stays flat as well. minimizes the analyst participation, which is important since nowadays various data-integration solutions are incorporated in real Database Management Systems (DBMS), such as Microsoft SQL Server DBMS [6]. Having a less analystdependent technique makes that operation of wide applicability, so that nonexpert users can apply it to their datasets.…”

Section: Experiments On the Publications Domainmentioning

confidence: 99%

Self-tuning in Graph-Based Reference Disambiguation

Nuray-Turan

Kalashnikov

Advances in Databases: Concepts, Systems and Applications

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.