A multi-terabyte relational database for geo-tagged social network data

Dobos, László; Szüle, János; Bodnár, Tamás; Hanyecz, Tamas; Sebők, Tamás; Kondor, Dániel; Kallus, Zsófia; Stéger, József; Csabai, István; Vattay, Gábor

doi:10.1109/coginfocom.2013.6719259

Cited by 22 publications

(39 citation statements)

References 16 publications

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“…Tweets were marked as coming from the USA if their longitude fell between -130 and -70 degrees and their latitude between 24 and 52 degrees. The messages and their metadata were organised into a large relational database that enabled fast and efficient querying (Dobos, 2013) at the Department of Physics of Complex Systems of Eotvos Lorand University, Budapest.…”

Section: Methodsmentioning

confidence: 99%

Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Kmetty¹,

Koltai

Bokányi³

et al. 2017

Intersections

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Section: Methodsmentioning

confidence: 99%

Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Kmetty¹,

Koltai

Bokányi³

et al. 2017

Intersections

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“…Other results propose Optics [46] and grid-based [45] clustering to compile a structure of locations for GPS trajectory mining. Similar to our result, in [15], GADM is used over the same Twitter data set, but only for visualization purposes.…”

Section: Related Workmentioning

confidence: 65%

“…We use a four-month collection of 400 million geo-tagged Twitter messages detailed in [15]. We mention that the metadata of tweets may contain not only GPS coordinates but also a place attribute that can contain the name and type of the place.…”

Section: Data Setmentioning

confidence: 99%

Location-aware online learning for top-k recommendation

Pálovics

Szalai

Pap

et al. 2017

Pervasive and Mobile Computing

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We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain.For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods.Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message.

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“…The other system [5] uses MSSQL for data storage. They use the advantages of MSQL to build indices for localization.…”

Section: Related Workmentioning

confidence: 99%

Virtual observatory for twitter messages

Gombos

Vincellér

Kiss

2015

Proceedings of the 9th International Conference on Applied Informatics, Volume 1

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A new research direction has emerged as the investigation of On-line Social Networks. Twitter is one of the most well-known social networks. Analysis of the Twitter is easier than other social networks because it provides the opportunity for collecting and downloading of a certain percentage of the messages without any restrictions. There are several researches on topics as detecting news and events, human behaviors, analyzing and mining of opinions. The on-line messages are available only through a continuous stream. To store the messages from the stream effectively and efficiently is a serious challenge against software system design and architecture. Every day about 10 GBs data are generated by this way and storing of this volume of data is not an easy task. In this paper we present a technique and architecture for collecting and storing the messages of the Twitter, and we present a prototype where data can be accessed for further analysis. Our system makes use specific techniques and methods of Oracle environment. Our software architecture approach is in contrast to previous solutions in which the systems use MSSQL or MySQL DBMS. We demonstrate that indexing and Job scheduler of the Oracle provide advantages to retrieve and handle large amounts of data.

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A multi-terabyte relational database for geo-tagged social network data

Cited by 22 publications

References 16 publications

Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Seasonality Pattern of Suicides in the US – a Comparative Analysis of a Twitter Based Bad-mood Index and Committed Suicides

Location-aware online learning for top-k recommendation

Virtual observatory for twitter messages

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