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Amanpreet Singh Saini

Publications and source records attributed to Amanpreet Singh Saini.

2 recordsLinked to original sources

Cops and Lethal Robber on Rings: A Distributed Perspective

The Cops and Robber game is extensively studied in the sequential setting where the main goal is to capture the robber. Capturing the robber means at least one cop, and the robber will be at the same vertex together at some time. There are several variants, including variants where the goal of the cops is to surround the robber. Surrounding the robber means there is at least one cop in each of the neighboring vertices of the robber's position. In this paper, we introduce it in the distributed setting while empowering the robber by saying it can even kill cops. Specifically, in our model, the robber moves in odd rounds and has unbounded speed, cops move in even rounds, and if one or more cops move into a vertex where the robber is currently residing, all these cops get killed. We call this {\it lethal robber}. This also connects our work to the Intruder Capture and Black Hole Search problems by introducing an entity which is dynamic as well as lethal, a setting that, to the best of our knowledge, has not been studied. In this work, we introduce the lethal robber, define the {\it cops and lethal robber} problem in the distributed setting and study it on a static ring of size $n$. We prove $n$ cops are not enough, even if all start from the same vertex, and provide an algorithm starting from an arbitrary initial configuration that requires $n+\lfloor\log n \rfloor+4$ cops in the worst case.

cs.DC↗

Toward Data Cleaning with a Target Accuracy: A Case Study for Value Normalization

Many applications need to clean data with a target accuracy. As far as we know, this problem has not been studied in depth. In this paper we take the first step toward solving it. We focus on value normalization (VN), the problem of replacing all string that refer to the same entity with a unique string. VN is ubiquitous, and we often want to do VN with 100% accuracy. This is typically done today in industry by automatically clustering the strings then asking a user to verify and clean the clusters, until reaching 100% accuracy. This solution has significant limitations. It does not tell the users how to verify and clean the clusters. This part also often takes a lot of time, e.g., days. Further, there is no effective way for multiple users to collaboratively verify and clean. In this paper we address these challenges. Overall, our work advances the state of the art in data cleaning by introducing a novel cleaning problem and describing a promising solution template.

cs.DB↗