Big Data solutions on a small scale
Evaluating accessible high-performance computing for social research
Bibliographic Data
| ID | 5260412 |
|---|---|
| Authors | Dhiraj Murthy (0000-0001-9734-1124, Goldsmiths University of London, corresponding author), Sawyer A Bowman, Sawyer Bowman (Bowdoin College) |
| Year | 2014 |
| Volume | 1 |
| Issue | 2 |
| Pages | 1/2/2053951714559105 |
| Publication date | 2014-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2053951714559105 |
| OpenAlex | W2104939292 |
| Language | EN |
| Citations received | 8 |
| References cited | 23 |
Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high demand for this type of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental case study (in the growing field of social Twitter analytics), we found that not only are solutions like Cloudera's Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines
Analytics · Big data · Cloud computing · Data mining · Data science · World Wide Web · Big Data and Business Intelligence · Cloud Computing and Resource Management · Computer Science · Scientific Computing and Data Management
Śmieci na wejściu, śmieci na wyjściu”. Wpływ jakości koderów na działanie sieci neuronowej klasyfikującej wypowiedzi w mediach społecznościowych
How to prepare data for the automatic classification of politically related beliefs expressed on Twitter? The consequences of researchers’ decisions on the number of coders, the algorithm learning procedure, and the pre-processing steps on the performance of supervised models
Scaling up Content Analysis
Applying Big Data visualization to detect trends in 30 years of performance reports
Big data e news online
Urban Social Media Demographics
Do We Tweet Differently From Our Mobile Devices? A Study of Language Differences on Mobile and Web-Based Twitter Platforms
Comparative Process-oriented Research Using Social Media and Historical Text
| Unique citing works | 8 |
|---|---|
| Citations per year | 0,73 |
| Citation span | 2015 - 2023 (9) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 7 |