Scalable processing of spatial-keyword queries:
Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is be...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2019]
|
| Schriftenreihe: | Synthesis lectures on data management
#56 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is being generated at massive scale. Almost all online transactions have an associated spatial trace. The spatial trace is derived from GPS coordinates, IP addresses, or cell-phone-tower locations. Hundreds of millions or even billions of spatial-keyword objects are being generated daily. Spatial-keyword data has numerous applications that require efficient processing and management of massive amounts of spatial-keyword data. This book starts by overviewing some important applications of spatial-keyword data, and demonstrates the scale at which spatial-keyword data is being generated. Then, it formalizes and classifies the various types of queries that execute over spatial-keyword data. Next, it discusses important and desirable properties of spatial-keyword query languages that are needed to express queries over spatial-keyword data. As will be illustrated, existing spatial-keyword query languages vary in the types of spatial-keyword queries that they can support. There are many systems that process spatial-keyword queries. Systems differ from each other in various aspects, e.g., whether the system is batch-oriented or stream-based, and whether the system is centralized or distributed. Moreover, spatial-keyword systems vary in the types of queries that they support. Finally, systems vary in the types of indexing techniques that they adopt. This book provides an overview of the main spatial-keyword data-management systems (SKDMSs), and classifies them according to their features. Moreover, the book describes the main approaches adopted when indexing spatial-keyword data in the centralized and distributed settings. Several case studies of SKDMSs are presented along with the applications and query types that these SKDMSs are targeted for and the indexing techniques they utilize for processing their queries. Optimizing the performance and the query processing of SKDMSs still has many research challenges and open problems. The book concludes with a discussion about several important and open research-problems in the domain of scalable spatial-keyword processing |
| Beschreibung: | Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on February 26, 2019) |
| Beschreibung: | 1 Online-Resource (xvii, 98 Seiten) Illustrationen |
| ISBN: | 9781681734880 |
| DOI: | 10.2200/S00892ED1V01Y201901DTM056 |
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| 520 | |a Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is being generated at massive scale. Almost all online transactions have an associated spatial trace. The spatial trace is derived from GPS coordinates, IP addresses, or cell-phone-tower locations. Hundreds of millions or even billions of spatial-keyword objects are being generated daily. Spatial-keyword data has numerous applications that require efficient processing and management of massive amounts of spatial-keyword data. This book starts by overviewing some important applications of spatial-keyword data, and demonstrates the scale at which spatial-keyword data is being generated. Then, it formalizes and classifies the various types of queries that execute over spatial-keyword data. | ||
| 520 | |a Next, it discusses important and desirable properties of spatial-keyword query languages that are needed to express queries over spatial-keyword data. As will be illustrated, existing spatial-keyword query languages vary in the types of spatial-keyword queries that they can support. There are many systems that process spatial-keyword queries. Systems differ from each other in various aspects, e.g., whether the system is batch-oriented or stream-based, and whether the system is centralized or distributed. Moreover, spatial-keyword systems vary in the types of queries that they support. Finally, systems vary in the types of indexing techniques that they adopt. This book provides an overview of the main spatial-keyword data-management systems (SKDMSs), and classifies them according to their features. Moreover, the book describes the main approaches adopted when indexing spatial-keyword data in the centralized and distributed settings. | ||
| 520 | |a Several case studies of SKDMSs are presented along with the applications and query types that these SKDMSs are targeted for and the indexing techniques they utilize for processing their queries. Optimizing the performance and the query processing of SKDMSs still has many research challenges and open problems. The book concludes with a discussion about several important and open research-problems in the domain of scalable spatial-keyword processing | ||
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| spelling | Mahmood, Ahmed R. Verfasser (DE-588)1189747146 aut Scalable processing of spatial-keyword queries Ahmed R. Mahmood and Walid G. Aref [San Rafael, California] Morgan & Claypool Publishers [2019] © 2019 1 Online-Resource (xvii, 98 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on data management #56 Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on February 26, 2019) Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is being generated at massive scale. Almost all online transactions have an associated spatial trace. The spatial trace is derived from GPS coordinates, IP addresses, or cell-phone-tower locations. Hundreds of millions or even billions of spatial-keyword objects are being generated daily. Spatial-keyword data has numerous applications that require efficient processing and management of massive amounts of spatial-keyword data. This book starts by overviewing some important applications of spatial-keyword data, and demonstrates the scale at which spatial-keyword data is being generated. Then, it formalizes and classifies the various types of queries that execute over spatial-keyword data. Next, it discusses important and desirable properties of spatial-keyword query languages that are needed to express queries over spatial-keyword data. As will be illustrated, existing spatial-keyword query languages vary in the types of spatial-keyword queries that they can support. There are many systems that process spatial-keyword queries. Systems differ from each other in various aspects, e.g., whether the system is batch-oriented or stream-based, and whether the system is centralized or distributed. Moreover, spatial-keyword systems vary in the types of queries that they support. Finally, systems vary in the types of indexing techniques that they adopt. This book provides an overview of the main spatial-keyword data-management systems (SKDMSs), and classifies them according to their features. Moreover, the book describes the main approaches adopted when indexing spatial-keyword data in the centralized and distributed settings. Several case studies of SKDMSs are presented along with the applications and query types that these SKDMSs are targeted for and the indexing techniques they utilize for processing their queries. Optimizing the performance and the query processing of SKDMSs still has many research challenges and open problems. The book concludes with a discussion about several important and open research-problems in the domain of scalable spatial-keyword processing Querying (Computer science) Keyword searching Spatial data infrastructures Abfragesprache (DE-588)4134011-5 gnd rswk-swf Abfragesprache (DE-588)4134011-5 s DE-604 Aref, Walid G. Verfasser aut Erscheint auch als Druck-Ausgabe, hardcover 978-1-68173-489-7 Erscheint auch als Druck-Ausgabe, paperback 978-1-68173-487-3 Synthesis lectures on data management #56 (DE-604)BV036731811 56 https://doi.org/10.2200/S00892ED1V01Y201901DTM056 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Mahmood, Ahmed R. Aref, Walid G. Scalable processing of spatial-keyword queries Synthesis lectures on data management Querying (Computer science) Keyword searching Spatial data infrastructures Abfragesprache (DE-588)4134011-5 gnd |
| subject_GND | (DE-588)4134011-5 |
| title | Scalable processing of spatial-keyword queries |
| title_auth | Scalable processing of spatial-keyword queries |
| title_exact_search | Scalable processing of spatial-keyword queries |
| title_full | Scalable processing of spatial-keyword queries Ahmed R. Mahmood and Walid G. Aref |
| title_fullStr | Scalable processing of spatial-keyword queries Ahmed R. Mahmood and Walid G. Aref |
| title_full_unstemmed | Scalable processing of spatial-keyword queries Ahmed R. Mahmood and Walid G. Aref |
| title_short | Scalable processing of spatial-keyword queries |
| title_sort | scalable processing of spatial keyword queries |
| topic | Querying (Computer science) Keyword searching Spatial data infrastructures Abfragesprache (DE-588)4134011-5 gnd |
| topic_facet | Querying (Computer science) Keyword searching Spatial data infrastructures Abfragesprache |
| url | https://doi.org/10.2200/S00892ED1V01Y201901DTM056 |
| volume_link | (DE-604)BV036731811 |
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