Demonstration of Taghreed: A system for querying, analyzing, and visualizing geotagged microblogs

Magdy, Amr; Alarabi, Louai; Al-Harthi, Saif; Musleh, Mashaal; Ghanem, Thanaa M.; Ghani, Sulaiman Ab; Basalamah, Saleh; Mokbel, Mohamed F.

doi:10.1109/icde.2015.7113390

Cited by 28 publications

(46 citation statements)

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“…• User analysis [14], [18], [22], [45]: This work is mainly interested in analyzing user information to Techniques for both real-time and historical data TweeQL [26] MDMS [23] Taghreed [22] EarlyBird [6] EarlyBird [6] TI [8] LSII [46] Mercury [24] SocialSearch [20] MDMS [23] AFIA [39] GeoScope [5] Mercury [24] AFIA [39] Taghreed [22] MDMS [23] Venus [25] Provenance [47] Topsy Political Index [44] EvenTweet [1] StreamCube [11] Shaker [36] Situation [38] TweetTracker [42] Political Index [44] TwitInfo [27] Jury [7] UserRec [14] NewsRec [32] SentClass [3] Seman [28] Twanchor [30] VisCAT [12] mapD [15] TwitInfo [27] Taghreed [22] TwitterStand [37] TweetTracker [42] UserRec [14] TopUsers [18] Taghreed [22] IdUsers [45] LocInfer [17] LocIden …”

Section: Part I: Microblogs Data Analysismentioning

confidence: 99%

“…• Visual analysis [12], [15], [21], [27], [37], [42]: This work either explores visual analysis by itself [12], [15] or associate it with other analysis tasks [21], [27], [37], [42] to visually explore the output. In general, different types of graphs and maps are used visually to summarize the large amounts of microblogs data and its contents.…”

Section: Part I: Microblogs Data Analysismentioning

confidence: 99%

“…• Indexing and query processing: This work includes various indexing techniques that have been proposed to index incoming microblogs either in memory [5], [6], [22], [24], [39], [46], [47] or in disk [8], [22]. This includes keyword search based on temporal ranking [6], [8], single-attribute search based on generic ranking functions [46], spatial-aware search that exploits location information in microblogs [24], and personalized social-aware search that exploits the social graph and produces user-specific search results [20].…”

Section: Part Ii: Microblogs Data Managementmentioning

confidence: 99%

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Microblogs data management and analysis

Magdy

Mokbel

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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Microblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including detecting and analyzing events, user analysis for geo-targeted ads and political elections, and critical applications like discovering health issues and rescue services. Consequently, major research efforts are spent to analyze and manage microblogs data to support different applications. In this tutorial, we give a 1.5 hours overview about microblogs data analysis, management, and systems. The tutorial gives a comprehensive review for research efforts that are trying to analyze microblogs contents to build on them new functionality and use cases. In addition, the tutorial reviews existing research that propose core data management components to support microblogs queries at scale. Finally, the tutorial reviews system-level issues and on-going work on supporting microblogs data through the rising big data systems. Through its different parts, the tutorial highlights the challenges and opportunities in microblogs data research.

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Section: Part I: Microblogs Data Analysismentioning

confidence: 99%

Section: Part I: Microblogs Data Analysismentioning

confidence: 99%

Section: Part Ii: Microblogs Data Managementmentioning

confidence: 99%

See 1 more Smart Citation

Microblogs data management and analysis

Magdy

Mokbel

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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“…However, their approach was batch-based, and this paper focused on stream-based analysis. Taghreed [29] can achieve high insertion rates, and low query latency for tweets. Their approach focused on geotagged tweets with spatial and temporal boundaries, which enables implementation of optimizations.…”

Section: Comparison To Literaturementioning

confidence: 99%

“…The system scales in terms of processing time, when data size and number of instances are increased. Taghreed is a system for scalable querying and visualization of geotagged tweets/microblogs [29]. The system is able to manage, query, and visualize billions of tweets with main memory indexes, an optimized query engine, a recovery manager, and an interactive visualizer.…”

mentioning

confidence: 99%

Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing

Pääkkönen

2016

J Big Data

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IntroductionStreaming data is increasingly important for online services. Facebook [1] and LinkedIn [2] have analysed event related data for understanding usage in their ecosystems. Twitter has created a big data streaming architecture, which is able to serve and process thousands of tweets in a second [3][4][5]. Also, several systems [6], methods [7,8], and benchmarking tools [9][10][11] have been created for facilitating implementation of tweet related processing and analysis by 3rd parties. Especially, new stream processing technologies (Spark [12], AsterixDB [13]) have been created, which could be selected for implementation of stream extraction, storage and analysis functionalities. Although stream processing performance has been studied [12,14], comparative feasibility analysis of the technologies has not been extensively performed in the context of semi-structured data processing. AbstractFor getting up-to-date insight into online services, extracted data has to be processed in near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) analyse streaming data for development of new services. Several technologies have been developed, which could be selected for implementation of stream processing functionalities. The contribution of this paper is feasibility analysis of technologies for stream-based processing of semi-structured data. Particularly, feasibility of a Big Data management system for semi-structured data (AsterixDB) will be compared to Spark streaming, which has been integrated with Cassandra NoSQL database for persistence. The study focuses on stream processing in a simulated social media use case (tweet analysis), which has been implemented to Eucalyptus cloud computing environment on a distributed shared memory multiprocessor platform. The results indicate that AsterixDB is able to provide significantly better performance both in terms of throughput and latency, when data feed functionality of AsterixDB is used, and stream processing has been implemented with Java. AsterixDB also scaled on the same level or better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of data, when tweets were streamed into the database with data feeds. Pääkkönen J Big Data (2016) Big Data (2016) 3:6 This article focuses on performance analysis of Spark streaming, Cassandra, and AsterixDB technologies for stream processing of semi-structured social media data (tweets). Especially, Spark streaming has been integrated with Cassandra for data persistence, which has been compared to AsterixDB on Eucalyptus cloud environment on a DSM multiprocessor platform. The results indicated that AsterixDB achieved significantly higher throughput and lower latency, when data feeds were utilized and stream processing was implemented with Java. Performance of AsterixDB also scaled better, when the amount of nodes on the cloud platform was increased. However, stream processing in AsterixDB was delayed by batching of streamed tw...

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Efficient Top K Temporal Spatial Keyword Search

Zhang

Zhu

et al. 2018

Lecture Notes in Computer Science

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Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper,we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques .

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Demonstration of Taghreed: A system for querying, analyzing, and visualizing geotagged microblogs

Cited by 28 publications

References 5 publications

Microblogs data management and analysis

Microblogs data management and analysis

Feasibility analysis of AsterixDB and Spark streaming with Cassandra for stream-based processing

Efficient Top K Temporal Spatial Keyword Search

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