26 research outputs found
Mining people's semantic trajectory behaviours from geotagged photographs
No full textWeb 2.0 technology has changed the way users use the web. In Web 2.0, users can create their own data and upload to the web. The new web technology promotes the evolution of social media applications built on Web 2.0. Social media allow the creation and exchange of user-generated content. One main type of social media is photo-sharing websites where the main objective is the sharing of photo media content between users. Through these websites users store and manage their photos, and share and communicate with friends, families and colleagues.
The development of mobile devices facilitates people's easy usage of the Internet. This has lead to dramatic growth in the number of user-generated photos in the website. This massive collection of photo data may enclose people's movement behaviours, which are useful to domain experts and areas such as traffic management and tourism. However, this large and complex dataset requires advanced techniques to extract the hidden useful knowledge from the big data.
Some previous studies have been conducted, and various approaches have been proposed to extract people's movement behaviours from online geotagged photos. These studies are mainly about three topics. The first topic is to reveal the spatial behaviours of people that the approaches detect the spatial locations that people prefer to visit (Kisilevich, Mansmann, and Keim 2010; Lee, Cai, and Lee 2013). The second topic is to find out the spatial place association rules that determine the sets of places visited together in people's movements. The third topic is to discover people's dynamic spatiotemporal movement behaviours including the spatio-temporal traffic ow (Girardin et al. 2008b) and frequent spatio-temporal movement patterns (Zheng, Zha, and Chua 2012; Cai et al. 2014; Bermingham and Lee 2014).
However, previous approaches lack consideration of the additional aspatial semantics information of trajectories. They are traditional geometric-feature analyses. The main drawback of previous methods is that their result patterns contain only pure geometric data, without meaningful semantics information about the mobility. Most applications analyses require complementing trajectory with additional information from the application context. The contextual information provides useful knowledge about moving behaviours with richer and more meaningful semantic information and the semantic-level patterns.
This thesis aims to develop a systematic framework for extraction of people's movement patterns with meaningful and understandable semantics information. We add the aspatial semantics annotations to trajectories and analyse trajectories with spatial, temporal and aspatial features together. We aim to find the semantics-enhanced movement patterns, including semantic sequential patterns, semantic common patterns and semantic trajectory patterns. Finally, this thesis also aims to build an itinerary recommender system based on the extracted trajectory patterns.
In this thesis, we propose a systematic framework for discovery of people's semantic mobility patterns from geotagged photos. The framework has four main functions for extraction of the three semantic patterns and for building the recommender system, respectively. At the first step, the framework builds spatio-temporal trajectory data from the geotagged photos. Then, we add background geographic information, place type annotation and multiple environmental contextual data to the raw trajectories to generate people's semantic trajectories.
From the semantic trajectories, the framework's first main function is to find out the frequent semantic sequential patterns. This thesis proposes a sequential pattern mining method to extract semantic sequential patterns, which are sequences of stops that frequently occur in people's trajectories. This method can deal with multi-dimensional semantic trajectories. The extracted groups of patterns include not only the basic patterns, which contain geographic place category information only, but also the multi-dimensional semantic patterns, which are associated with flexible combinations of frequent environmental contextual information.
The framework's second main function is to reveal the semantic common patterns. This thesis proposes a semantic trajectory clustering approach to find semantic common patterns in the semantic trajectories. The common pattern shows the common track drawn from many people having similar trajectories. A distance function is designed and proposed for the multidimensional semantic trajectories.
The third main function of the framework is to extract the semantic trajectory patterns. This thesis presents a semantic trajectory pattern mining method to find frequent trajectory patterns from semantic trajectories. A semantic trajectory pattern demonstrates a frequently visited sequence of stops with typical transition time information. The transition time shows the time interval between two stops that indicates temporal behaviour of people's mobility.
Finally, this framework builds a recommendation system based on the extracted semantically enhanced movement patterns. The system provides users with suggestions about travel itineraries including travel route and time interval information between two stops. The system is semantic-aware, allowing users to customise sets of place types that they want to visit in the trip and to set up travel duration.
We conduct experiments to evaluate proposed methods using real photo dataset from Flickr1. The experimental results prove the effectiveness of our framework. The results show that the proposed semantics added trajectory analysis methods can extract detailed and semantically enhanced semantic patterns that not only show people's semantic-level mobility patterns, but also provide rich meaningful information and better understanding of people's movements. The results also demonstrate that our recommender system effectively generates a set of customised and targeted semantic-level itineraries that meet the user-specified constraints and with an efficiency itinerary generation property. In addition, our system produces higher place type-layer itineraries with richer meaningful information about travel contexts
Mining points-of-interest association rules from geo-tagged photos
No full textThe advent of photo-sharing services results in massive user-generated geo-tagged photos. These photos implicitly and explicitly indicate points-ofinterest and their associations. This study aims to combine two data mining techniques: clustering and association rules mining to mine areas of attraction, and their associative patterns. We analyze photos from Flickr in the area of Queensland, Australia, a popular tourist destination hosting the Great Barrier Reef and tropical rain forest. We report interesting experimental results and discuss findings
Points-of-interest mining from people's photo-taking behavior
No full textMillions of geo-tagged photos are becoming available due to the widespread of photo-sharing websites. These social medias capture attractive points-of-interest and contain interesting photo-taking patterns. Massive amount of these user-oriented data produces new challenges and understanding people's photo-taking behavior is of great importance for local tourism-related businesses. This paper analyzes geo-tagged photos from Flickr for Queensland, a tourism-intensive and the second largest state in Australia. We report interesting points-of-interest patterns and discuss these findings
Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos
No full textA large number of geo-tagged photos become available online due to the advances in geo-tagging services and Web technologies. These geo-tagged photos are indicative of photo-takers’ trails and movements, and have been used for mining people movements and trajectory patterns. These geo-tagged photos are inherently spatio-temporal, sequential and implicitly containing aspatial semantics. and recommender systems are collaborative filtering based. There have been some studies to build itinerary recommender systems from these geo-tagged photos, but they fail to consider these dimensions and share some common drawbacks, especially lacking aspatial semantics or temporal information. This paper proposes an itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos by discovering sequential points-of-interest with temporal information from other users’ visiting sequences and preferences. Our system considers spatio-temporal, sequential, and aspatial semantics dimensions, and also takes into account user-specified preferences and constraints to customise their requests. It generates a set of customised and targeted semantic-level itineraries meeting the user specified constraints. The proposed method generates these semantic itineraries from historic people’s movements by mining frequent travel patterns from geo-tagged photos. Experimental results demonstrate the informativeness, efficiency and effectiveness of our proposed method over traditional approaches
Sentiment Clustering with Topic and Temporal Information from Large Email Dataset
No full textSentiment analysis with features addition to opinion words has been an appealing area in recent studies. Some research has been conducted for finding relationship between sentiments, topics and temporal sentiment analysis. Nevertheless, Email sentiment analysis received relatively less attention due to the complexity of its structure and indirectness of its language. This paper introduces a systematic framework for sentiment clustering using topic and temporal features for large Email datasets. Interesting Email and sentiment distribution patterns are summarized and discussed with empirical results
A framework for mining semantic-level tourist movement behaviours from geo-tagged photos
No full textThis study investigates tourist movement patterns on the type of place semantic-level. We extract the semantic common movement patterns that a group of tourists have similar movement trajectories on the semantic level, and find out semantic trajectory patterns which are sequences of the type of place objects with transit time. Using real geo-tagged photos, we find out interesting common movement patterns and trajectory patterns. These results provide richer information and understanding of tourist movement behaviour on the type of place semantic-level
Discovering common semantic trajectories from geo-tagged social media
No full textMassive social media data are being created and uploaded to online nowadays. These media data associated with geographical information reflect people's footprints of movements. This study investigates into extraction of people's common semantic trajectories from geo-referenced social media data using geo-tagged images. We first convert geo-tagged photographs into semantic trajectories based on regions-of-interest, and then apply density-based clustering with a similarity measure designed for multi-dimensional semantic trajectories. Using real geo-tagged photographs, we find interesting people's common semantic mobilities. These semantic behaviors demonstrate the effectiveness of our approach
Mining semantic sequential patterns from geo-tagged photos
No full textSocial media data associated with geographic location and time information reflect people footprint in real world. Abundance of geo-referenced content represents a massive opportunity to understanding of human geographic mobility behaviors. Most trajectory mining research from geo-enabled social media data focus on spatial geometric features. Integrating trajectory analysis with semantic information can implicate human movement behaviors on semantic levels. In this work, we illustrate a study on mining semantically enriched trajectory patterns using geo-referenced content especially using geo-tagged photo data for case study. We first propose a semantic region of interest mining technique to extract reference regions with semantic information. We then present a multi-dimensional sequential pattern mining algorithm to find trajectory patterns with various semantic dimension combinations. We apply our method to real geo-tagged photo data to discover interesting patterns about sequential movement related to multiple semantics. Experimental results show that our method is able to find useful semantic trajectory patterns from geo-tagged content and deal with multi-dimensional semantic trajectories
Exploration of geo-tagged photos through data mining approaches
No full textWith the development of web technique and social network sites human now can produce information, share with others online easily. Photo-sharing website, Flickr, stores huge number of photos where people upload and share their pictures. This research proposes a framework that is used to extract associative points-of-interest patterns from geo-tagged photos in Queensland, Australia, a popular tourist destination hosting the great Barrier Reef and tropical rain forest. This framework combines two popular data mining techniques: clustering for points-of-interest detection, and association rules mining for associative points-of-interest patterns. We report interesting experimental results and discuss findings
Mining semantic trajectory patterns from geo-tagged data
No full textUser-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge
