RLT-S: A Web System for Record Linkage

Abstract Background Record linkage integrates records across multiple related data sources identifying duplicates and accounting for possible errors. Real life applications require efficient algorithms to merge these voluminous data sources to find out all records belonging to same individuals. Our recently devised highly efficient record linkage algorithms provide best-known solutions to this challenging problem. Method We have developed RLT-S, a freely available web tool, which implements our single linkage clustering algorithm for record linkage. This tool requires input data sets and a small set of configuration settings about these files to work efficiently. RLT-S employs exact match clustering, blocking on a specified attribute and single linkage based hierarchical clustering among these blocks. Results RLT-S is an implementation package of our sequential record linkage algorithm. It outperforms previous best-known implementations by a large margin. The tool is at least two times faster for any dataset than the previous best-known tools. Conclusions RLT-S tool implements our record linkage algorithm that outperforms previous best-known algorithms in this area. This website also contains necessary information such as instructions, submission history, feedback, publications and some other sections to facilitate the usage of the tool. Availability RLT-S is integrated into http://www.rlatools.com, which is currently serving this tool only. The tool is freely available and can be used without login. All data files used in this paper have been stored in https://github.com/abdullah009/DataRLATools. For copies of the relevant programs please see https://github.com/abdullah009/RLATools

Efficient Algorithms for Fast Integration on Large Data Sets from Multiple Sources

Background Recent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently. Methods Hierarchical clustering based solutions are used to integrate multiple (in particular more than two) datasets. Edit distance is used as the basic distance calculation, while distance calculation of common input errors is also studied. Several techniques have been applied to improve the algorithms in terms of both time and space: 1) Partial Construction of the Dendrogram (PCD) that ignores the level above the threshold; 2) Ignoring the Dendrogram Structure (IDS); 3) Faster Computation of the Edit Distance (FCED) that predicts the distance with the threshold by upper bounds on edit distance; and 4) A pre-processing blocking phase that limits dynamic computation within each block. Results We have experimentally validated our algorithms on large simulated as well as real data. Accuracy and completeness are defined stringently to show the performance of our algorithms. In addition, we employ a four-category analysis. Comparison with FEBRL shows the robustness of our approach. Conclusions In the experiments we conducted, the accuracy we observed exceeded 90% for the simulated data in most cases. 97.7% and 98.1% accuracy were achieved for the constant and proportional threshold, respectively, in a real dataset of 1,083,878 records

Extraction of activity patterns on large video recordings

International audienceExtracting the hidden and useful knowledge embedded within video sequences and thereby discovering relations between the various elements to help an efficient decision-making process is a challenging task. The task of knowledge discovery and information analysis is possible because of recent advancements in object detection and tracking. The authors present how video information is processed with the ultimate aim to achieve knowledge discovery of people activity and also extract the relationship between the people and contextual objects in the scene. First, the object of interest and its semantic characteristics are derived in real-time. The semantic information related to the objects is represented in a suitable format for knowledge discovery. Next, two clustering processes are applied to derive the knowledge from the video data. Agglomerative hierarchical clustering is used to find the main trajectory patterns of people and relational analysis clustering is employed to extract the relationship between people, contextual objects and events. Finally, the authors evaluate the proposed activity extraction model using real video sequences from underground metro networks (CARETAKER) and a building hall (CAVIAR)

Profiling and Grouping Space-time Activity Patterns of Urban Individuals

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