MALOS

MALOS is a project on Movement-Aware Location Selection. Given the movement history (e.g., historical trajactory/check-in logs) for massive users, how to find a location, with or without a group of candidates, to place some facilities, such that the new facility can probably exhibit the best (marginal) utility (e.g., Min-dist or Max-inf).

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PINOCCHIO

Given the movement history (e.g., historical trajactory/check-in logs) for massive users, how to find a location, from a group of candidates, to place some facility, such that the new facility can probably cover the most number of moving users. We present a novel model, namely PINOCCHIO, that can address the aforementioned problem efficiently. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for PINOCCHIO.

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MILE-RUN

Given the movement history (e.g., historical trajactory/check-in logs) for massive users, how to find a location to deploy the next facility, while there are k existing ones, such that the aggregated utility for the facility network is maximized. We present a novel model, namely MILE-RUN, that can address the aforementioned problem efficiently. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for MILE-RUN.

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k-CollectiveFP

Given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for k-CollectiveFP.

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FROST

Facility relocation (FR) problem, which aims to optimize the placement of facilities to accommodate the changes of users’ locations, has a broad spectrum of applications. Despite the significant progress made by existing solutions to the FR problem, they all assume each user is stationary and represented as a single point. Unfortunately, in reality, objects (e.g., people, animals) are mobile. For example, a car-sharing user picks up a vehicle from a station close to where she is currently located. Consequently, these efforts may fail to identify superior solution to the FR problem. In this paper, for the first time, we take into account movement history of users and introduce a novel FR problem, called motion-fr, to address the above limitation.

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MALOS Publications:

MALOS Group Members: