Berkay Aydin

A filter-and-refine approach to mine spatiotemporal co-occurrences

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time. However, the discovery of STCOPs in data sets with extended spatial representations that evolve over time is computationally expensive because of the necessity to calculate interest measures to assess the co-occurrence strength, and the number of candidates for STCOPs growing exponentially with the number of spatiotemporal event types. In this paper, we introduce a novel and effective filter-and-refine algorithm to efficiently find prevalent STCOPs in massive spatiotemporal data repositories with polygon shapes that move and evolve over time. We provide theoretical analysis of our approach, and follow this investigation with a practical evaluation of our algorithm effectiveness on three real-life data sets and one artificial data set.

Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns

In this paper, we investigate using specifically-designated spatiotemporal indexing techniques for mining cooccurrence patterns from spatiotemporal datasets with evolving polygon-based representations. Previously, suggested techniques for spatiotemporal pattern mining algorithms did not take spatiotemporal indexing techniques into account. We present a new framework for mining spatiotemporal co-occurrence patterns that can use various indexing techniques for efficiently accessing data. Two well-studied spatiotemporal indexing structures, Scalable and Efficient Trajectory Index (SETI) and Chebyshev Polynomial Indexing are currently implemented and available in our framework.

Time-efficient significance measure for discovering spatiotemporal co-occurrences from data with unbalanced characteristics

Mining spatiotemporal co-occurrence patterns requires assessing the strength of co-occurrences among the instances of different feature types. Currently, a spatiotemporal version of the Jaccard measure is used for measuring the strength of spatiotemporal co-occurrences. We present an extended spatiotemporal version of the Jaccard measure (J*) that is more relevant and efficient for the task of STCOP mining. We also demonstrate the space and time efficiency of the J* with experimental evaluation.

Spatiotemporal event sequence mining from evolving regions

In this paper, we introduce methods for mining spatiotemporal event sequences from event datasets with evolving region objects. Spatiotemporal event sequences are the ordered lists of event types whose event instances frequently follow each other in spatiotemporal context. Two Apriori-based algorithms are designed for the task of spatiotemporal event sequence mining. We provide explanations for interestingness measures we employed. We present extended experimental results that demonstrate the computational efficiency of our methods.

Mining spatiotemporal co-occurrence patterns in non-relational databases

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.

Spatiotemporal Frequent Pattern Mining on Solar Data: Current Algorithms and Future Directions

In this paper, we present the current work and future directions on spatiotemporal frequent pattern mining algorithms for mining solar data. The current spatiotemporal pattern mining algorithms focus on spatiotemporal co-occurrence patterns. We reveal four types of spatiotemporal concepts that can be mined from solar data: event sequences, periodicity, spatiotemporal convergence and propagation. Throughout the paper, we exhibit examples of these concepts in the solar physics domain, and present related algorithms and the challenges of mining these concepts from solar data.

Spatio-temporal interpolation methods for solar events metadata

This paper introduces three interpolation methods that enrich complex evolving region trajectories that are captured every day from numerous ground-based and space-based solar observatories. The interpolation module takes a trajectory as its input and generates an enriched trajectory with interpolated time-geometry pairs. we created three different interpolation techniques that are: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), and FP-Interpolation (Filament Polygon Interpolation). The methods combine K-means clustering algorithm, shape signature representation, and linear interpolation to generate the missing polygons. This is the first research of this kind that attempts to address the problem of solar big data interpolation. Finally, we outline future improvements and opportunities for solar data interpolation.

A Graph-Based Approach to Spatiotemporal Event Sequence Mining

Sequential pattern mining from spatiotemporal data has received much attention in recent years due to its broad application domains such as targeted advertising, location prediction for taxi services, and urban planning. The characteristics of spatiotemporal sequences vary widely depending on the discovered knowledge type. Most of the recent approaches focus on the point-based spatiotemporal data presumably because of its greater availability. However, the region-based spatiotemporal data, primarily obtained from scientific resources, has not received much attention. In this work, we introduce an algorithm for mining spatiotemporal event sequences (STESs) from trajectory-based event instances. We consider each instance to be associated with an event type. We propose a graph-based mining algorithm, which transforms the sequences of spatiotemporal trajectories into a directed acyclic graph, and discovers the frequently occurring sequences of event types. Our proposed algorithm adopts a pattern-growth based approach utilizing the directed edges from the graph and discovers the event sequences without expensive candidate generation steps.

Filling the Gaps in Solar Big Data: Interpolation of Solar Filament Event Instances

This paper introduces a new interpolation method that fills the gap in missing solar filament big data that are captured every day from numerous ground-based and space-based observatories. It proposes a new algorithm that takes two filament event instances and interpolates between them given a cadence. The method combines K-means clustering algorithm, time series shape representation, and linear interpolation to generate the missing filament polygons. This is the first research of this kind that attempts to address the problem of solar big data interpolation. We evaluate the proposed method using area, shape, and distance accuracy criteria. Finally, we outline future improvements and opportunities for solar data interpolation.

Measuring the Significance of Spatiotemporal Co-Occurrences

Spatiotemporal co-occurrences are the appearances of spatial and temporal overlap relationships among trajectory-based spatiotemporal instances with region-based geometric representations. Assessing the significance of spatiotemporal co-occurrences plays an important role in the spatiotemporal frequent pattern mining applications of moving region objects. A spatiotemporal version of the popular Jaccard measure has been used for measuring the strength of spatiotemporal co-occurrences. We will demonstrate the shortcomings of the Jaccard (J) measure when it is used for assessing the significance of co-occurrences among spatiotemporal instances with highly different spatiotemporal evolution characteristics. We will present two extended novel measures (J+ and J*) that address the problems linked to the J measure. Our work includes algorithms for the significance measure calculations, the proofs and explanations about the key properties of measures, and a detailed experimental evaluation section. Our experiments include in-depth relevancy and running time analyses demonstrating the suitability of our proposed measures for spatiotemporal frequent pattern mining algorithms.