Attributed graph is widely used for modeling a variety of information networks, such as the web, sensor networks, biological networks, economic graphs, and social networks. Given the high popularity of attributed graph, in this paper, we study one of the most fundamental graph query types for attributed graph - the reachability query with attribute constraints i.e. `Is there a path from source to destination such that all attributes on the path satisfy given attribute constraints?'. We first introduce a new approach which takes the advantage of a `perfect' hash function for compressing a multi-dimensional attribute into a unique hash value so as to bound the expected cost of secondary storage access (e.g. SDD I/O, remote data access). Then, we propose a synopsis based heuristic search technique to further reduce the CPU and secondary storage access cost. For both techniques, the index construction time and space are still linear to the topology size. An extensive experimental evaluation using real and synthetic graphs demonstrates the superiority of our proposed techniques.

Many types of human mobility data, such as flows of taxicabs, card swiping data of subways, bike trip data and Call Details Records (CDR), can be modeled by a Spatio-Temporal Graph (STG). STG is a directed graph in which vertices and edges are associated with spatio-temporal properties (e.g. the traffic flow on a road and the geospatial location of an intersection). In this paper, we instantly detect interesting phenomena, entitled black holes and volcanos, from an STG. Specifically, a black hole is a subgraph (of an STG) that has the overall inflow greater than the overall outflow by a threshold, while a volcano is a subgraph with the overall outflow greater than the overall inflow by a threshold (detecting volcanos from an STG is proved to be equivalent to the detection of black holes). The online detection of black holes/volcanos can timely reflect anomalous events, such as disasters, catastrophic accidents, and therefore help keep public safety. The patterns of black holes/volcanos and the relations between them reveal human mobility patterns in a city, thus help formulate a better city planning or improve a system's operation efficiency. Based on a well-designed STG index, we propose a two-step black hole detection algorithm: The first step identifies a set of candidate grid cells to start from; the second step expands an initial edge in a candidate cell to a black hole and prunes other candidate cells after a black hole is detected. Then, we adapt this detection algorithm to a continuous black hole detection scenario. We evaluate our method based on Beijing taxicab data and the bike trip data in New York, finding urban anomalies and human mobility patterns.

This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence approach.The system is made available to the user as an adaptive mobile application, which allows a high degree of customization in recommending services and resources according to his/her current position and global profile.A case study applied to the town of Pittsburgh has been analyzed considering various users (with different profiles as visitors, students, professors) and an experimental campaign has been conducted obtaining interesting results.

Average case time complexity is widely used to evaluate the efficiency of an algorithm [2].Given a blackbox function, if a tester wants to know the average case time complexity, he/she has to analyze the source code and make input assumption so as to know the average case of the function.Although that is feasible, it is time consuming and that makes the testing process no longer automatic.In this paper, we propose an approach for estimating the average case time complexity of a given black-box function without analyzing source codes.Experimental results show that our approach can accurately estimate the average case time complexity without reading the source code.

A moving kNN query continuously reports the k results (restaurants) nearest to a moving query point (tourist). In addition to the query results, a service provider often returns to a mobile client a safe region that bounds the validity of query results in order to minimize the communication cost between the service provider and that mobile client. However, when a service provider is not trustworthy, it may send inaccurate query results or incorrect safe regions to mobile clients. In this paper, we present a framework for authenticating both the query results and the safe regions of moving kNN queries. We theoretically proved that our methods for authenticating moving kNN queries minimize the data sent between the service provider and the mobile clients. Extensive experiments are carried out using both real and synthetic data sets and results show that our methods can perform moving kNN query authentication with small communication costs and overhead.

A moving range query continuously reports the query result (e.g., restaurants) that are within radius $r$ from a moving query point (e.g., moving tourist). To minimize the communication cost with the mobile clients, a service provider that evaluates moving range queries also returns a safe region that bounds the validity of query results. However, an untrustworthy service provider may report incorrect safe regions to mobile clients. In this paper, we present efficient techniques for authenticating the safe regions of moving range queries. We theoretically proved that our methods for authenticating moving range queries can minimize the data sent between the service provider and the mobile clients. Extensive experiments are carried out using both real and synthetic datasets and results show that our methods incur small communication costs and overhead.

Data cube is a key element in supporting fast OLAP. Traditionally, an aggregate function is used to compute the values in data cubes. In this paper, we extend the notion of data cubes with a new perspective. Instead of using an aggregate function, we propose to build data cubes using the skyline operation as the “aggregate function.” Data cubes built in this way are called “group-by skyline cubes” and can support a variety of analytical tasks. Nevertheless, there are several challenges in implementing group-by skyline cubes in data warehouses: 1) the skyline operation is computational intensive, 2) the skyline operation is holistic, and 3) a group-by skyline cube contains both grouping and skyline dimensions, rendering it infeasible to precompute all cuboids in advance. This paper gives details on how to store, materialize, and query such cubes.

A moving kNN query continuously reports the k nearest neighbors of a moving query point. In addition to the query result, a service provider that evaluates moving queries often returns mobile clients a safe region that bounds the validity of query results to minimize the communication cost between the two parties. However, when a service provider is not trustworthy, it may send inaccurate query results or incorrect safe regions to clients. In this paper, we present a framework and algorithms to authenticate results and safe regions of moving kNN queries. Extensive experiments on both real and synthetic datasets show that our methods are efficient in terms of both computation time and communication costs.