Name
Abstract | ||
|---|---|---|
With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2963143 | ACM Comput. Surv. |
Keywords | Field | DocType |
Big Data,Analytics,Workflow,Taxonomy,Scheduling,Storage,Intelligent Assistance,Orchestration,analytics talent gap,consumable analytics | Data science,Data mining,Business analytics,Software analytics,Computer science,Business intelligence,Analytics,Orchestration (computing),Big data,Ecosystem | Journal |
Volume | Issue | ISSN |
49 | 2 | 0360-0300 |
Citations | PageRank | References |
5 | 0.58 | 40 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shady Khalifa | 1 | 5 | 2.27 |
| Yehia Elshater | 2 | 11 | 1.74 |
| Kiran Sundaravarathan | 3 | 5 | 0.58 |
| Aparna Balachandra Bhat | 4 | 5 | 0.58 |
| patrick martin | 5 | 148 | 18.22 |
| Fahim T. Imam | 6 | 22 | 5.84 |
| D. Rope | 7 | 7 | 1.98 |
| Mike McRoberts | 8 | 5 | 0.92 |
| Craig Statchuk | 9 | 8 | 3.40 |