KNN-Based outlier detection over IoT streaming data is a fundamental problem, which has many applications. However, due to its computational complexity, existing efforts cannot efficiently work in the IoT streaming data. In this paper, we propose a novel framework named GAAOD(Grid-based Approximate Average Outlier Detection) to support KNN-Based outlier detection over IoT streaming data. Firstly, GAAOD introduces a grid-based index to manage summary information of streaming data. It can self-adaptively adjust the resolution of cells, and achieve the goal of efficiently filtering objects that almost cannot become outliers. Secondly, GAAOD uses a min-heap-based algorithm to compute the distance upper- /lower-bound between objects and their k-th nearest neighbors respectively. Thirdly, GAAOD utilizes a k- skyband based algorithm to maintain outliers and candidate outliers. Theoretical analysis and experimental results verify the efficiency and accuracy of GAAOD.

In this paper, we study the Time-Dependent k Nearest Neighbor (TD-kNN) query on moving objects that aims to return k objects arriving at the query location with the least traveling cost departing at a given time t. Although the kNN query on moving objects has been widely studied in the scenario of the static road network, the TD-kNN query tends to be more complicated and challenging because under the time-dependent road network, the cost of each edge is measured by a cost function rather than a fixed distance value. To tackle such difficulty, we adopt the framework of GLAD and develop an advanced index structure to support efficient fastest travel cost query on time-dependent road network. In particular, we propose the Time-Dependent H2H (TD-H2H) index, which pre-computes the aggregated weight functions between each node to some specific nodes in the decomposition tree derived from the road network. Additionally, we establish a grid index on moving objects for candidate object retrieval and location update. To further accelerate the TD-kNN query, two pruning strategies are proposed in our solution. Apart from that, we extend our framework to tackle the time-dependent approachable kNN (TD-AkNN) query on moving objects targeting for the application of taxi-hailing service, where the moving object might have been occupied. Extensive experiments with different parameter settings on real-world road network show that our solutions for both TD-kNN and TD-AkNN queries are superior to the competitors in orders of magnitude.

Relational keyword search (R-KWS) systems provide users with a convenient means in relational database queries. There exist two main types of R-KWS: those based on Data Graphs and those based on Schema Graphs. In this paper, we focus on the latter, R-KWS based on Schema Graphs. Most existing methods are typically inefficient due to the large number of repetitive operation caused by overlapping candidate networks and the execution of lots of complex queries. We present the R-KWS approach based on combined candidate network evaluation to improve the query efficiency. The proposed Combined Candidate Network (CCN) can efficiently share the overlapping part between candidate networks, and then avoid the repetitive operation during the evaluation of candidate networks. Meanwhile, CCN possesses another important characteristic that candidate networks within a CCN are still identifiable after candidate networks being compressed into a CCN. We design an algorithm based on this characteristic to evaluate CCN for the generation of final query results. This algorithm is able to eliminate the execution of a large number of complex queries required by most existing approaches, and thus significantly improve the efficiency of keyword search. Experiments on real datasets show that our approach can improve query efficiency without any loss of the quality of query results with respect to existing approaches.

With the development of location-based services, smart cities, and intelligent transportation, route planning has evolved beyond shortest path finding to satisfy user’s flexible travel purposes through the Optimal Routes with Collective Spatial Keywords (ORCSK) routing. Because different Points of Interest (POIs) contain different sets of keywords, the user usually needs to visit multiple POIs to fulfill all needs. Moreover, the POIs’ stop hardness (time and cost) also influences user experience, but it was ignored by the existing solutions. Therefore, this work proposes to extend the ORCSK problem into Skyline Optimal Routes with Collective Spatial Keyword (Sky-ORCSK) by considering both distance and stop hardness. Specifically, we first propose the IG-Sky algorithm from the spatial keyword search perspective by extending the IG-Tree. Then we propose the DA-Sky algorithm from the path enumeration perspective by extending our previous DA-CSK. Furthermore, five optimization strategies are proposed to improve efficiency by pruning the search space. Extensive experimental evaluations on real-world datasets demonstrate the algorithms’ efficacy and reliability, marking a significant step forward in refined route planning for modern urban environments.