Location Based Services (LBS) have become extremely popular over the past decade, being used on a daily basis by millions of users. Instances of real-world LBS range from mapping services (e.g., Google Maps) to lifestyle recommendations (e.g., Yelp) to real-estate search (e.g., Redfin). In general, an LBS provides a public (often web-based) search interface over its backend database (of tuples with 2D geolocations), taking as input a 2D query point and returning k tuples in the database that are closest to the query point, where k is usually a small constant such as 20 or 50. Such a public interface is often called a k-Nearest-Neighbor, i.e., kNN, interface. In this paper, we consider a novel problem of enabling density based clustering over the backend database of an LBS using nothing but limited access to the kNN interface provided by the LBS. Specifically, a key limit enforced by most real-world LBS is a maximum number of kNN queries allowed from a user over a given time period. Since such a limit is often orders of magnitude smaller than the number of tuples in the LBS database, our goal here is 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 conduct a comprehensive set of experiments over benchmark datasets and popular real-world LBS such as Yahoo! Flickr, Zillow, Redfin and Google Maps and demonstrate the effectiveness of our proposed techniques.

We propose to demonstrate HDBExpDetector, a system for analysts to discover bursty events from a thirdparty web database by using nothing but the existing search interface of the web database to monitor and detect sudden changes to aggregates that satisfy certain user-defined conditions. Video https://youtu.be/7Pwd8o1h5CY.

Location Based Services (LBS), including standalone ones such as Google Maps and embedded ones such as “users near me” in the WeChat instant-messaging platform, provide great utility to millions of users. Not only that, they also form an important data source for geospatial and commercial information such as Point-Of-Interest (POI) locations, review ratings, user geo-distributions, etc. Unfortunately, it is not easy to tap into these LBS for tasks such as data analytics and mining, because the only access interface they offer is a limited k-Nearest-Neighbor (kNN) search interface - i.e., for a given input location, return the k nearest tuples in the database, where k is a small constant such as 50 or 100. This limited interface essentially precludes the crawling of an LBS' underlying database, as the small k mandates an extremely large number of queries that no real-world LBS would allow from an IP address or API account. We demonstrate ANALOC, a web based system that enables fast analytics over an LBS by issuing a small number of queries through its restricted kNN interface. ANALOC stands in sharp contrast with existing systems for analyzing geospatial data, as those systems mostly assume complete access to the underlying data. Specifically, ANALOC supports the approximate processing of a wide variety of SUM, COUNT and AVG aggregates over user-specified selection conditions. In the demonstration, we shall not only illustrate the design and accuracy of our underlying aggregate estimation techniques, but also showcase how these estimated aggregates can be used to enable exciting applications such as hotspot detection, infographics, etc. Our demonstration system is designed to query real-world LBS (systems or modules) such as Google Maps, WeChat and Sina Weibo at real time, in order to provide the audience with a practical understanding of the performance of ANALOC.

Numerous web databases, e.g., amazon.com, eBay.com, are "hidden" behind (i.e., accessible only through) their restrictive search and browsing interfaces. This demonstration showcases HDBTracker, a web-based system that reveals and tracks (the changes of) user-specified aggregate queries over such hidden web databases, especially those that are frequently updated, by issuing a small number of search queries through the public web interfaces of these databases. The ability to track and monitor aggregates has applications over a wide variety of domains - e.g., government agencies can track COUNT of openings at online job hunting websites to understand key economic indicators, while businesses can track the AVG price of a product over a basket of e-commerce websites to understand the competitive landscape and/or material costs. A key technique used in HDBTracker is RS-ESTIMATOR, the first algorithm that can efficiently monitor changes to aggregate query answers over a hidden web database.