Moving Phenomenon: Aggregation and Analysis of Geotime-Tagged Contents on the Web

Kim, Kyoung-Sook; Zettsu, Koji; Kidawara, Yutaka; Kiyoki, Yasushi

doi:10.1007/978-3-642-10601-9_2

Cited by 11 publications

(10 citation statements)

References 18 publications

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“…[20] treats aggregation of sequenced spatiotemporal data in the form of points and lines, but not regions. Kim [7] views the changes to regions as a continuously changing phenomenon with respect to time and generates a space-time volume from known discrete positions of the region, similar to the approach described in this paper. However, Kim does not perform aggregates with respect to temporal coverage.…”

Section: Aggregatesmentioning

confidence: 95%

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Operations to support temporal coverage aggregates over moving regions

et al. 2016

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A temporal coverage operation computes the duration that a moving object covers a spatial area. We extend this notion into temporal coverage aggregates, in which the spatial area covered for a maximum or minimum amount of time by a moving region, or set of moving regions, is discovered. We define the max temporal aggregate coverage operation and the min temporal aggregate coverage operation. We provide an algorithm to compute these operations, and show that it is correct. Finally, the algorithm is implemented in the open source, Pyspatiotemporalgeom library to verify the algorithm under a variety of test cases.

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Section: Aggregatesmentioning

confidence: 95%

“…Additional research on aggregate operations over spatiotemporal data includes [7,19,20]. [19] uses histograms to compute aggregates from a database, rather than streamingstyle data.…”

Section: Aggregatesmentioning

confidence: 99%

Operations to support temporal coverage aggregates over moving regions

et al. 2016

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“…In [3], we focused on the creation of spatiotemporal shape of a moving phenomenon by using a set of geotime-tagged contents; however, we reverse the operation in this study. We presume we already knew the spatiotemporal region of interest of a moving phenomenon because it is a familiar real-world event or a series of events, such as hurricanes, flooding, or earthquakes.…”

Section: Phenomena-based Content Managementmentioning

confidence: 99%

“…We have already proposed a new concept "moving phenomenon" and simple visual elements to represent the aggregation of geotime-tagged contents with spatial, temporal, and thematic aspects in [3]. In this paper, we consider how to aggregate the contents with spatial, temporal, and thematic constraints dynamically and interactively.…”

Section: Introductionmentioning

confidence: 99%

Phenomena-Based Management of Geotime-Tagged Contents on the Web

Kim

Zettsu

Kidawara

et al. 2010

2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications

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Recently, Geoweb services help people easily create and share geo-spatiotemporal contents through Internet. As a result, it becomes an emerging issue to manage Geoweb contents in exploding lots of spatiotemporal information and to obtain comprehensive knowledge about the real world using web contents. This paper proposes a novel approach for managing geotime-tagged contents on the basis of moving phenomena, such as typhoons, earthquakes, and flooding. In this study, a moving phenomenon is represented by a predefined three-dimensional object in a spatiotemporal domain with predefined meanings, and we define a phenomena-based predicate template named Relates as a combination of spatial, temporal, and thematic constraints. By using the Relates predicate, we can dynamically aggregate geotime-tagged contents along three aspects: location, time, and topic. Moreover, we present an application of the phenomena-based contents management, called k-Sticker. k-Sticker allows users to find top-k terms related to their own moving phenomenon through a threedimensional (two spatial dimensions and one temporal dimension) visualization method.

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“…Their work is similar to the work in [68] but these authors focus on analysing the continuous spatio-temporal trends and their movements in a space-time domain. They employ their moving-phenomena data model as proposed in [46] to represent topic movements from the tweets. The system architecture is presented in Figure 2.8.…”

Section: Event Tracking In Social Networkmentioning

confidence: 99%

Event detection in social networks

Unankard¹

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Micro-blog services such as Twitter generate a large amount of messages carrying event information and users' opinions over a wide range of topics. The events discussed on social networks can be associated with topics, locations, and time periods. The events can be a variety, such as celebrities or political affairs, local social events, accidents, protests, or natural disasters. Messages are posted by users after they have experienced or witnessed the events happening in the real-world and they want to share their experiences immediately. People also express themselves spontaneously with respect to the social events in their social networks. Alternatively, policy-makers may want to know the feelings of users for a particular event to make informed decisions. With the increasing number of real-world events that are originated and discussed over social networks, event detection and tracking is becoming a compelling research issue. However, the traditional approaches to event detection and event tracking on large text streams are not applicable because of the following problems. First, they are not designed to deal with a large number of short and noisy messages. Second, social networks contain network structures such as friends, followers, replies, and re-tweets. Third, social network messages are associated with locations, which can be either senders' current locations or event locations. Fourth, each message is also associated with a timestamp. Messages often contain revealing and timely event information however, traditional text processing approaches assume documents are non-temporal. Moreover, given a particular time frame and a location the user is interested in, events that occurred in the given time frame from the chosen area are more valuable than others. Finding localized events has not been well studied yet.The goals of this thesis are to: (1) identify subsistent problems and challenges in event detection and tracking in streaming micro-blog text, (2) design approaches for event detection and event tracking in social networks, (3) design approaches for sentiment analysis for given event topics, and (4) evaluate the proposed approaches in real-world streaming datasets.In this thesis, our research is considered in three parts. Firstly, in order to detect emerging events from a large number of short and noisy messages, we propose an approach for the early detection of emerging hotspot events in social networks with location sensitivity. An algorithm is designed for slang conversion, synonym expansion and conceptual similarity to provide a rich semantic context for measuring message similarity to improve clustering results. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the hotspot emerging events. A sliding window manager is used to keep track of messages arriving in the system. The size of the sliding window is defined as the time interval. We evaluate our...

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Moving Phenomenon: Aggregation and Analysis of Geotime-Tagged Contents on the Web

Cited by 11 publications

References 18 publications

Operations to support temporal coverage aggregates over moving regions

Operations to support temporal coverage aggregates over moving regions

Phenomena-Based Management of Geotime-Tagged Contents on the Web

Event detection in social networks

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