A Novel Approach to Mining Travel Sequences Using Collections of Geotagged Photos View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010-03-31

AUTHORS

Slava Kisilevich , Daniel Keim , Lior Rokach

ABSTRACT

In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The mined data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our framework mines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In the second step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimarães, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework. More... »

PAGES

163-182

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-12326-9_9

DOI

http://dx.doi.org/10.1007/978-3-642-12326-9_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031114160


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/16", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Studies in Human Society", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Human Geography", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Konstanz, Konstanz, Germany", 
          "id": "http://www.grid.ac/institutes/grid.9811.1", 
          "name": [
            "University of Konstanz, Konstanz, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kisilevich", 
        "givenName": "Slava", 
        "id": "sg:person.013060614167.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013060614167.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Konstanz, Konstanz, Germany", 
          "id": "http://www.grid.ac/institutes/grid.9811.1", 
          "name": [
            "University of Konstanz, Konstanz, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Keim", 
        "givenName": "Daniel", 
        "id": "sg:person.0635776571.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635776571.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Information Systems Engineering and The Deutsche Telekom Laboratories, Ben-Gurion University of the Negev, Beer-Sheva, Israel", 
          "id": "http://www.grid.ac/institutes/grid.7489.2", 
          "name": [
            "Department of Information Systems Engineering and The Deutsche Telekom Laboratories, Ben-Gurion University of the Negev, Beer-Sheva, Israel"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rokach", 
        "givenName": "Lior", 
        "id": "sg:person.010734441055.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010734441055.55"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2010-03-31", 
    "datePublishedReg": "2010-03-31", 
    "description": "In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The mined data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our framework mines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In the second step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimar\u00e3es, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework.", 
    "editor": [
      {
        "familyName": "Painho", 
        "givenName": "Marco", 
        "type": "Person"
      }, 
      {
        "familyName": "Santos", 
        "givenName": "Maribel Yasmina", 
        "type": "Person"
      }, 
      {
        "familyName": "Pundt", 
        "givenName": "Hardy", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-12326-9_9", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-642-12325-2", 
        "978-3-642-12326-9"
      ], 
      "name": "Geospatial Thinking", 
      "type": "Book"
    }, 
    "keywords": [
      "geotagged photos", 
      "travel sequences", 
      "density-based clustering algorithm", 
      "mining frequent sequences", 
      "novel approach", 
      "frequent sequences", 
      "clustering algorithm", 
      "nearby points", 
      "main steps", 
      "sequence patterns", 
      "photos", 
      "photo locations", 
      "framework", 
      "unknown point", 
      "third step", 
      "semantics", 
      "mining", 
      "case study", 
      "algorithm", 
      "valuable information", 
      "first step", 
      "second step", 
      "objects", 
      "step", 
      "attractive place", 
      "capability", 
      "information", 
      "final step", 
      "collection", 
      "location", 
      "sequence", 
      "interest", 
      "trajectories", 
      "point", 
      "data", 
      "people", 
      "patterns", 
      "place", 
      "antecedents", 
      "mine", 
      "approach", 
      "Berlin", 
      "individuals", 
      "region", 
      "Guimar\u00e3es", 
      "study", 
      "length", 
      "Portugal", 
      "Germany", 
      "immediate antecedents", 
      "paper"
    ], 
    "name": "A Novel Approach to Mining Travel Sequences Using Collections of Geotagged Photos", 
    "pagination": "163-182", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1031114160"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-12326-9_9"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-12326-9_9", 
      "https://app.dimensions.ai/details/publication/pub.1031114160"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-10-01T06:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/chapter/chapter_324.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-12326-9_9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-12326-9_9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-12326-9_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-12326-9_9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-12326-9_9'


 

This table displays all metadata directly associated to this object as RDF triples.

137 TRIPLES      22 PREDICATES      75 URIs      68 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-12326-9_9 schema:about anzsrc-for:16
2 anzsrc-for:1604
3 schema:author N392562faa290474f922b5ce036c9f4c5
4 schema:datePublished 2010-03-31
5 schema:datePublishedReg 2010-03-31
6 schema:description In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The mined data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our framework mines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In the second step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimarães, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework.
7 schema:editor N6b206b0a4c844d7fa403f3e3c5b0c6c6
8 schema:genre chapter
9 schema:isAccessibleForFree true
10 schema:isPartOf Nbe3baea93fe243aaaae72962e92eded9
11 schema:keywords Berlin
12 Germany
13 Guimarães
14 Portugal
15 algorithm
16 antecedents
17 approach
18 attractive place
19 capability
20 case study
21 clustering algorithm
22 collection
23 data
24 density-based clustering algorithm
25 final step
26 first step
27 framework
28 frequent sequences
29 geotagged photos
30 immediate antecedents
31 individuals
32 information
33 interest
34 length
35 location
36 main steps
37 mine
38 mining
39 mining frequent sequences
40 nearby points
41 novel approach
42 objects
43 paper
44 patterns
45 people
46 photo locations
47 photos
48 place
49 point
50 region
51 second step
52 semantics
53 sequence
54 sequence patterns
55 step
56 study
57 third step
58 trajectories
59 travel sequences
60 unknown point
61 valuable information
62 schema:name A Novel Approach to Mining Travel Sequences Using Collections of Geotagged Photos
63 schema:pagination 163-182
64 schema:productId Nbbd853e41c854ae6a5a6524d84623373
65 Nc69a408c387849848470bddc5f084051
66 schema:publisher N1f86b83b91804c5f8567610cc944ed6b
67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031114160
68 https://doi.org/10.1007/978-3-642-12326-9_9
69 schema:sdDatePublished 2022-10-01T06:57
70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
71 schema:sdPublisher N4ff856aafd8f48a5b53679b02d767519
72 schema:url https://doi.org/10.1007/978-3-642-12326-9_9
73 sgo:license sg:explorer/license/
74 sgo:sdDataset chapters
75 rdf:type schema:Chapter
76 N145d8cf8e5fa460598f37d2c00d1104c schema:familyName Santos
77 schema:givenName Maribel Yasmina
78 rdf:type schema:Person
79 N1f86b83b91804c5f8567610cc944ed6b schema:name Springer Nature
80 rdf:type schema:Organisation
81 N392562faa290474f922b5ce036c9f4c5 rdf:first sg:person.013060614167.52
82 rdf:rest N7f0363e1da92492b8356667df5664951
83 N474a764ef65b4f0ba285a48fb67c8edd rdf:first Nb2b1e74c503547bfbec22b0bcd88ea23
84 rdf:rest rdf:nil
85 N4ff856aafd8f48a5b53679b02d767519 schema:name Springer Nature - SN SciGraph project
86 rdf:type schema:Organization
87 N6b206b0a4c844d7fa403f3e3c5b0c6c6 rdf:first Nf09c89d7ecc746cfbd6c365d17b26032
88 rdf:rest Nb9cd8f8859964a6d913d7677069cd6c2
89 N7f0363e1da92492b8356667df5664951 rdf:first sg:person.0635776571.01
90 rdf:rest Na450240aed184dd6a05d7d0af3145333
91 Na450240aed184dd6a05d7d0af3145333 rdf:first sg:person.010734441055.55
92 rdf:rest rdf:nil
93 Nb2b1e74c503547bfbec22b0bcd88ea23 schema:familyName Pundt
94 schema:givenName Hardy
95 rdf:type schema:Person
96 Nb9cd8f8859964a6d913d7677069cd6c2 rdf:first N145d8cf8e5fa460598f37d2c00d1104c
97 rdf:rest N474a764ef65b4f0ba285a48fb67c8edd
98 Nbbd853e41c854ae6a5a6524d84623373 schema:name dimensions_id
99 schema:value pub.1031114160
100 rdf:type schema:PropertyValue
101 Nbe3baea93fe243aaaae72962e92eded9 schema:isbn 978-3-642-12325-2
102 978-3-642-12326-9
103 schema:name Geospatial Thinking
104 rdf:type schema:Book
105 Nc69a408c387849848470bddc5f084051 schema:name doi
106 schema:value 10.1007/978-3-642-12326-9_9
107 rdf:type schema:PropertyValue
108 Nf09c89d7ecc746cfbd6c365d17b26032 schema:familyName Painho
109 schema:givenName Marco
110 rdf:type schema:Person
111 anzsrc-for:16 schema:inDefinedTermSet anzsrc-for:
112 schema:name Studies in Human Society
113 rdf:type schema:DefinedTerm
114 anzsrc-for:1604 schema:inDefinedTermSet anzsrc-for:
115 schema:name Human Geography
116 rdf:type schema:DefinedTerm
117 sg:person.010734441055.55 schema:affiliation grid-institutes:grid.7489.2
118 schema:familyName Rokach
119 schema:givenName Lior
120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010734441055.55
121 rdf:type schema:Person
122 sg:person.013060614167.52 schema:affiliation grid-institutes:grid.9811.1
123 schema:familyName Kisilevich
124 schema:givenName Slava
125 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013060614167.52
126 rdf:type schema:Person
127 sg:person.0635776571.01 schema:affiliation grid-institutes:grid.9811.1
128 schema:familyName Keim
129 schema:givenName Daniel
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635776571.01
131 rdf:type schema:Person
132 grid-institutes:grid.7489.2 schema:alternateName Department of Information Systems Engineering and The Deutsche Telekom Laboratories, Ben-Gurion University of the Negev, Beer-Sheva, Israel
133 schema:name Department of Information Systems Engineering and The Deutsche Telekom Laboratories, Ben-Gurion University of the Negev, Beer-Sheva, Israel
134 rdf:type schema:Organization
135 grid-institutes:grid.9811.1 schema:alternateName University of Konstanz, Konstanz, Germany
136 schema:name University of Konstanz, Konstanz, Germany
137 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...