https://cyberleninka.org/article/n/854933.pdf

Social media analytics – Challenges in topic discovery, data collection, and data preparation

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

/pdf/next-generation-big-data-analytics-state-of-the-art-3oc9k4ked2.pdf

Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

Deep learning in big data Analytics: A comparative study

Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

https://www.mdpi.com/2220-9964/9/5/331/pdf

State-of-the-Art Geospatial Information Processing in NoSQL Databases

Planning interventions have been applied to improve the well‐being, hereafter happiness, of residents. The happiness in shrinking cities, in particular, becomes more critical since urban decline tends to induce an unequal and uneven distribution of care under a limited budget and human resources. Using geo‐tagged Twitter, census, and geospatial data on Detroit, Michigan, which is one of the well‐known shrinking cities in the U.S., the spatial distribution of sentiments, topics of tweets appeared, and the association between neighborhood conditions and the level of happiness were examined. The outcomes indicate that people in Detroit are posting happy tweets more than negative tweets. The downtown area holds both positive and negative hotspots, which are clustered around sports arenas and bars, respectively. Neighborhoods with young and well‐educated residents, situated close to amenities (i.e., recreation facilities, colleges, and commercial areas), and less crime tend to be happier. The use of SNS data could serve as a meaningful social listening tool to reconcile the declining urban vitality of neighborhoods since people interact with those spaces. Negative sentiments are attached to specific neighborhoods with certain conditions so that regeneration efforts should take place in neighborhoods with a higher priority.

Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data

Tweet My Street is a cross-disciplinary project exploring the extent to which data derived from Twitter can reveal more about spatial and temporal behaviours and the meanings attached to these locally. This is done with a longer-term view to supporting the coproduction and delivery of local services, complaint mechanisms and horizontal community support networks. The project has involved the development of a web-based software application capable of retrieving, storing and visualising geo-located "tweets" (and associated digital content) from Twitter's Firehose. This has been piloted in Newcastle upon Tyne (UK) and has proven a scalable tool that can aid the analysis of social media data geographically. Beyond explaining efforts to analyse pilot data via this software, this paper elucidates three methodological challenges encountered during early collaboration. These include issues relating to "proximity" with subjects, ethics and critical questions about scholars' digital responsibilities during the neogeographic turn.

/pdf/tweet-my-street-a-cross-disciplinary-collaboration-for-the-1sxsp2l5s8.pdf

Tweet My Street: A Cross-Disciplinary Collaboration for the Analysis of Local Twitter Data

Tweet my street: a cross-disciplinary collaboration for the analysis of local Twitter data

Social media is an increasingly popular method for people to share information and interact with each other. Analysis of social media data has the potential to provide useful insights in a wide range of domains including social science, advertising and policing. Social media information is produced in real-time, and so analysis that can give insights into events as they occur can be particularly valuable. Similarly, analytics platforms providing low latency query responses can improve the user experience for ad-hoc data exploration on historic data sets. However, the rate at which new data is generated makes it a real challenge to design a system that can meet both of these challenges. This paper describes the deisgn and evaluation of such a system. Firstly, it describes how a meta-analysis of the types of questions that were being asked of Twitter data led to the identification of a small set of queries that could be used to answer the majority of them. Secondly, it describes the design of a scalable platform for answering these and other queries. The architecture is described: it is cloud-based, and combines both continuous query, and noSQL database technology. Evaluation results are presented which show that the system can scale to process queries on streaming data arriving at the rate of the full Twitter firehose. Experiments show that queries on large repositories of stored historic data can also be answered with low latency. Finally, we present the results of queries that combine both streaming and historic data.

A Platform for Analysing Stream and Historic Data with Efficient and Scalable Design Patterns

The growth of mobile devices has significantly increased the velocity and volume of location-based data Whilst there is enormous potential for applications that exploit this data in real-time, storing and querying it in real-time creates significant challenges Traditional RDBMS systems are not sufficiently scalable, while typical cloud-based solutions such as map-reduce do not possess the capabilities required for real-time, spatial-data processing Therefore, new approaches are needed In this paper we explore the use of NoSQL technologies These offer scalability, availability and fault tolerance, but -- as we show -- do not perform well with spatial data Therefore, in this paper we address this challenge by enhancing existing spatial indexing structures with novel algorithms for inserting and searching spatial data We have implemented this in a NoSQL solution (Antares), and evaluated it against two other NoSQL solutions, and a range of indexing structures: Kd-Tree, Quad Tree and Geohashing The results show that Antares significantly outperforms the other approaches

Antares: A Scalable, Real-Time, Fault Tolerant Data Store for Spatial Analysis

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Tweet My Street: A Cross-Disciplinary Collaboration for the Analysis of Local Twitter Data

Tweet my street: a cross-disciplinary collaboration for the analysis of local Twitter data

A Platform for Analysing Stream and Historic Data with Efficient and Scalable Design Patterns

Antares: A Scalable, Real-Time, Fault Tolerant Data Store for Spatial Analysis