DBLOC: Density Based Clustering over LOCation Based Services
DBLOC: Density Based Clustering over LOCation Based Services
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DOI：
10.1145/3183713.3193561
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发表时间：
2018-05
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通讯作者：
Yeshwanth D. Gunasekaran;Md. Farhadur Rahman;Sona Hasani;Nan Zhang;Gautam Das
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作者：
Yeshwanth D. Gunasekaran;Md. Farhadur Rahman;Sona Hasani;Nan Zhang;Gautam Das
基于位置的服务(LBS)在过去十年中变得非常流行。广受欢迎的LBS提供从地图服务(如谷歌地图)到餐馆评论(如Yelp)和房地产搜索(如Zillow)的所有服务。这些应用的后端数据库可以是地理空间和商业信息的丰富数据源，例如兴趣点(POI)位置、评论、评级、用户地理分布等。然而，对后端数据库的访问通常受到LBS所有者提供的公共查询接口(通常基于网络)的限制。在大多数情况下，这些应用程序的公共搜索接口可以抽象地建模为KNN接口，以地理位置(即，纬度和经度)作为输入，并返回最接近查询点的前k个POI，其中k是一个小常量，例如50或100。由于这一限制，第三方用户在LBS上执行分析或挖掘变得极其困难。我们演示了DBLOC，这是一个基于Web的系统，它通过使用LBS提供的有限访问KNN接口来实现对LBS的分析。具体地说，使用DBLOC，用户可以在LBS的后端数据库上执行基于密度的集群。由于查询速率限制-即用户/IP地址在特定时间段内可以发出的KNN查询的最大数量，通常不可能访问LBS后端数据库中的所有元组。因此，DBLOC的目标是从LBS挖掘簇分配函数f(.)，使得对于数据库中的任何元组t(其可能已被访问或可能未被访问)，f(.)可以高精度地产生t的聚类分配。我们还展示了DBLOC如何使用户能够进一步分析所发现的簇，以便挖掘感兴趣的簇内/簇间信息。
Location Based Services (LBS) have become extremely popular over the past decade. Popular LBS run the entire gamut from mapping services (such as Google Maps) to restaurants reviews (such as Yelp) and real-estate search (such as Zillow). The backend database of these applications can be a rich data source for geospatial and commercial information such as Point-Of-Interest (POI) locations, reviews, ratings, user geo-distributions, etc. However, access to the backend database is often restricted by a public query interface (often web-based) provided by the LBS owners. In most cases the public search interface of these applications can be abstractly modeled as kNN interface, taking a geolocation (i.e., latitude and longitude) as input and returning top-k POI's that are closest to the query point, where k is a small constant such as 50 or 100. Because of this restriction it becomes extremely difficult for third-party users to perform analytics or mining over LBS. We demonstrate DBLOC, a web-based system that enables analytics over the LBS by using nothing but limited access to kNN interface provided by the LBS. Specifically, using DBLOC the users can perform density based clustering over the backend database of LBS. Due to query rate limit constraint - i.e., maximum number of kNN queries a user/IP address can issue over a specific period of time, it is often impossible to access all the tuples in backend database of an LBS. Thus, DBLOC aims to mine from the LBS a cluster assignment function f(.), such that for any tuple t in the database (which may or may not have been accessed), f(.) can produce the cluster assignment of t with high accuracy. We also demonstrate how DBLOC enables the users to further analyze the discovered clusters in order to mine interesting intra/inter cluster information.