Spatial association mining, as one of important techniques for spatial data mining, is used to discover interesting relationship patterns among spatial features based on spatial proximity from a large spatial database. Explosive growth in georeferenced data has emphasized the need to develop computationally efficient methods for analyzing big spatial data. Parallel and distributed computing is effective and mostly-used strategy for speeding up large scale dataset algorithms. This work presents parallel spatial association mining on the Spark RDD framework - a specially-designed in-memory parallel computing model to support iterative algorithms. The initial experiment result shows that the Spark-based algorithm has significantly improved performance than the method with MapReduce in spatial association pattern mining.

Social media is a source of real time data that individuals create and voluntarily share on major social media generators such as Facebook, Twitter, Google, Yahoo, and Instagram. Nowcasting uses real-time data and is defined as the prediction of the present, the very near future, and the very recent past. Nowcasting with social media assists businesses and government agencies in understanding public opinion and trends, and in creating timely forecasts of economic indicators. The example includes geo-economic events: how the public's sentiment affects the stock market [1], predicting political alignment: management of political strategy [4], and social questions: popular events associated with increased public sentiment [8]. Nowcasting and social media are also being used together to discover unusual social events such as demonstrations and spontaneous festivals, and natural disasters such as earthquakes and storms [6], and to forecast early detection of disease activity - thereby allowing rapid disease response, which reduces the public impact of disease [2].

Spatial association mining has been used for discovering frequent spatial association patterns from large static spatial databases. When a large spatial database is updated, it is computationally expensive to redo the pattern discovery process for the updated database. This work presents the problem of finding spatial association patterns incrementally from evolving databases which are constantly updated with fresh data. The proposed method is implemented on the MapReduce framework for large-scale spatial data processing, and empirically evaluated. The developed algorithm shows substantial performance improvements when compared with an iterative and non-incremental spatial association mining algorithm.

Spatial association rule mining is a useful tool for discovering interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies. The computation of co-location mining is prohibitively expensive with increase in data size and spatial neighborhood. In this work, we propose to parallelize spatial co-location mining on distributed machines. A framework of parallel co-location mining based on MapReduce is presented.

Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and a novel MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results also show the proposed method is computationally more efficient than naïve alternatives.

A spatial co-located event set represents a subset of spatial events whose instances are located in a spatial neighborhood. The discovery of co-evolving spatial event sets involves finding co-located event sets whose spatial prevalence variations over time are similar to a specific query sequence. Mining co-evolving spatial event sets is computationally challenging due to the high computational cost of finding co-located event instances on continuous geographic space, large temporal space and a composite interest measure, i.e., the spatial prevalence time sequence of a co-located event set. We propose a novel method for mining co-evolving spatial event sets. We analyze the proposed algorithm in terms of correctness and completeness, and experimentally evaluate the algorithm.

Spatial colocations represent the subsets of features which are frequently located together in geographic space. Colocation pattern discovery presents challenges since spatial objects are embedded in a continuous space, whereas classical data is often discrete. A large fraction of the computation time is devoted to identifying the instances of colocation patterns. We propose a novel joinless approach for efficient colocation pattern mining. The joinless colocation mining algorithm uses an instance-lookup scheme instead of an expensive spatial or an instance join operation for identifying colocation instances. We prove the joinless algorithm is correct and complete in finding colocation rules. We also describe a partial join approach for a spatial data set often clustered in neighborhood areas. We provide the algebraic cost models to characterize the performance dominance zones of the joinless method and the partial join method with a current join-based colocation mining method, and compare their computational complexities. In the experimental evaluation, using synthetic and real-world data sets, our methods performed more efficiently than the join-based method and show more scalability in dense data.

Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to identifying the instances of co-location patterns. We propose a novel join-less approach for co-location pattern mining, which materializes spatial neighbor relationships with no loss of co-location instances and reduces the computational cost of identifying the instances. The join-less co-location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-location instances. The experimental evaluations show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.

Co-location patterns are subsets of spatial features (e.g. freeways, frontage roads) usually located together in geographic space. Recent literature has provided a transaction-free approach to discover co-location patterns over spatial point data sets to avoid potential loss of proximity relationship information in partitioning continuous geographic space into transactions. This paper provides a more general transaction-free approach to mining data sets with extended spatial objects, e.g. line-strings and polygons. Key challenges include modeling of neighborhood and relationships among extended spatial objects as well as control of related geometric computation costs. The approach we propose is based on a new buffer-based definition of neighborhoods. Furthermore, we introduce and compare two pruning approaches, namely a prevalence-based pruning approach and a geometric filter-and-refine approach. Experimental evaluation with a real data set (a digital roadmap of the Minneapolis and St. Paul metropolitan area) shows that the geometric filter-and-refine approach can speed up the prevalence-based pruning approach by a factor of 30 to 40. Finally, we show how the extended co-location mining algorithm proposed in this paper has been used to find line-string co-location patterns, which can help with decision-makings on selecting most challenging field test routes. These field test routes are important for evaluating a GPS-based approach to accessing road user charges.

Nearest neighbor query is one of the most important operations in spatial databases and their application domains, e.g., location-based services, advanced traveler information systems, etc. This paper addresses the problem of finding the in-route nearest neighbor (IRNN) for a query object tuple which consists of a given route with a destination and a current location on it. The IRNN is a facility instance via which the detour from the original route on the way to the destination is smallest. This paper addresses four alternative solution methods. Comparisons among them are presented using an experimental framework. Several experiments using real road map datasets are conducted to examine the behavior of the solutions in terms of three parameters affecting the performance. Our experiments show that the computation costs for all methods except the precomputed zone-based method increase with increases in the road map size and the query route length but decreases with increase in the facility density. The precomputed zone-based method shows the most efficiency when there are no updates on the road map.