COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets

Processing count queries over event streams at multiple time granularities

Cataloged from PDF version of article.Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities. The proposed approximation method efficiently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on different real data sets including daily price changes in two different stock exchange markets. The obtained results show its effectiveness. (C) 2005 Elsevier Inc. All rights reserved

Discovery of Spatiotemporal Event Sequences

Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms

Dual sticky hierarchical Dirichlet process hidden Markov model and its application to natural language description of motions

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov modle (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. The number of HMMs and the number of topics are both automatically determined. The sticky prior avoids redundant states and makes our HDP-HMM more effective to model multimodal observations. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. The sources and sinks in the scene are learnt by clustering endpoints (origins and destinations of trajectories). The semantic motion regions are learnt using the points in trajectories. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequences of atomic activities. the action represented by the trajectory can be described in natural language in as autometic a way as possible.The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene

A Framework for Spatial Database Explanations

abstract: In the last few years, there has been a tremendous increase in the use of big data. Most of this data is hard to understand because of its size and dimensions. The importance of this problem can be emphasized by the fact that Big Data Research and Development Initiative was announced by the United States administration in 2012 to address problems faced by the government. Various states and cities in the US gather spatial data about incidents like police calls for service. When we query large amounts of data, it may lead to a lot of questions. For example, when we look at arithmetic relationships between queries in heterogeneous data, there are a lot of differences. How can we explain what factors account for these differences? If we define the observation as an arithmetic relationship between queries, this kind of problem can be solved by aggravation or intervention. Aggravation views the value of our observation for different set of tuples while intervention looks at the value of the observation after removing sets of tuples. We call the predicates which represent these tuples, explanations. Observations by themselves have limited importance. For example, if we observe a large number of taxi trips in a specific area, we might ask the question: Why are there so many trips here? Explanations attempt to answer these kinds of questions. While aggravation and intervention are designed for non spatial data, we propose a new approach for explaining spatially heterogeneous data. Our approach expands on aggravation and intervention while using spatial partitioning/clustering to improve explanations for spatial data. Our proposed approach was evaluated against a real-world taxi dataset as well as a synthetic disease outbreak datasets. The approach was found to outperform aggravation in precision and recall while outperforming intervention in precision.Dissertation/ThesisMasters Thesis Computer Science 201