scholarly journals Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 448 ◽  
Author(s):  
Reza Rawassizadeh ◽  
Chelsea Dobbins ◽  
Mohammad Akbari ◽  
Michael Pazzani
Keyword(s):  
Event Detection ◽  
Clustering Algorithm ◽  
Search Space ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Data Types ◽  
Contextual Data ◽  
User Behaviors ◽  
Clustering Approach ◽  
Spatio Temporal

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.

scholarly journals Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 4007
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Pedram Ghamisi ◽  
Behnood Rasti ◽  
Robert Jackisch ◽  
Paul Scheunders ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Multiple Data Sets ◽  
Imaging Sensors

The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.

Towards retrieving social events from spatio-temporal photo annotation

First Monday ◽  
2019 ◽  
Author(s):  
Davi Oliveira Serrano De Andrade ◽  
Anderson Almeida Firmino ◽  
Cláudio de Souza Baptista ◽  
Hugo Feitosa De Figueirêdo
Keyword(s):  
Clustering Algorithm ◽  
Geographical Location ◽  
Spatial Clusters ◽  
Public Database ◽  
Common Goal ◽  
Social Events ◽  
Clustering Approach ◽  
Spatio Temporal ◽  
Event Annotation ◽  
Photo Annotation

An event can be defined as a happening that gathers people with some common goal over a period of time and in a certain place. This paper presents a new method to retrieve social events through annotations in spatio-temporal photo collections, known as STEve-PR (Spatio-Temporal EVEnt Photo Retrieval). The proposed technique uses a clustering algorithm to gather similar photos by considering the location, date and time of the photos. The STEve-PR clustering approach clusters photos belonging to the same event. STEve-PR uses spatial clusters created to propagate event annotation between photos in the same cluster and employs TF-IDF similarity between tags to find the spatial cluster with the highest similarity for photos without a geographical location. We evaluated our approach on a public database.

Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering –

2017 ◽  
Vol 29 (2) ◽  
pp. 353-363 ◽  
Author(s):  
Yoshimi Ui ◽  
◽  
Yutaka Akiba ◽  
Shohei Sugano ◽  
Ryosuke Imai ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Series Data ◽  
Processing Unit

[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the<span class=”bold”>k</span>-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard.

Ontology With Hybrid Clustering Approach for Improving the Retrieval Relevancy in Social Event Detection

2018 ◽  
Vol 14 (4) ◽  
pp. 33-56 ◽  
Author(s):  
Sheba Selvam ◽  
Ramadoss Balakrishnan ◽  
Balasundaram Sadhu Ramakrishnan
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Digital Technology ◽  
Clustering Algorithm ◽  
Contextual Information ◽  
Incremental Clustering ◽  
Social Event ◽  
Hybrid Clustering ◽  
Social Event Detection ◽  
Clustering Approach

Progression in digital technology and the fame of social media sites such as Facebook, YouTube, Flickr etc., necessitate sharing memories. This results in a colossal amount of multimedia content such as text, audio, photographs and video on the web. Retrieving photographs exclusively from web in the large collection is a challenging task. One way to retrieve photographs is by identifying them as events. The automatic organization of a multimedia collection into groups of items, where each group corresponds to a distinct event is described as Social Event Detection (SED). Contextual information, present for each photograph in social media adds semantics to the photographs. For semantic based retrieval, ontology based approaches yield good retrieval results, by reducing the number of false positives. So, the proposed approach moves with domain ontology construction followed by a hybrid clustering approach. Compared to the existing single-pass incremental clustering algorithm, the proposed approach ensures a good f-measure of 0.8608.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

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