Interactions with big data analytics
By Danyel Fisher, R. Deline, Mary Czerwinski, Steven Mark Drucker. INTR, 2012
360 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/de29c51d169cdb5066f6832e9a8878900c43f100
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Rodrigo Neves Calheiros
- Rajkumar Buyya
- Yonggang Wen
- Marcos Dias de Assunção
- Tat-seng Chua
- Wei Fu
- Morgan Swink
- Tim Menzies
In Information Systems
- Rajkumar Buyya
- Rodrigo Neves Calheiros
- Yonggang Wen
- Wei Fu
- Tat-seng Chua
- Morgan Swink
- Tim Menzies
- Anastasios Tefas
In Information Systems and Management
- Rajkumar Buyya
- Rodrigo Neves Calheiros
- Wei Fu
- Morgan Swink
- Tim Menzies
- Ravi Srinivasan
- Jia Yu
- David Abramson
Citations in the corpus
- Learning Deep Representations with Probabilistic Knowledge Transfer (2018, background): “However, this renders most KT methods inappropriate for representation learning tasks that are needed for applications other than classification, such as text and multimedia information retrieval [8, 29], learning convolutional feature extractors for object detectors [36]/trackers [31], or interacti”
- Big Data computing and clouds: Trends and future directions (2013, background): “It is argued that a large part of data produced today is either unstructured or semi-structured .”
- An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective (2018, background): “…, data analytics , business analytics and supply chain analytics to describe capabilities that enable firms to collect and analyze data to gain critical insights (Agarwal and Dhar 2014, Davenport 2006, Fisher et al. 2012, Hsinchun et al. 2012, Nichols 2013, Sinha and Wege-ner 2013, Souza 2014).”
- Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “At the same time, the system usually involves multiple distinct phases for different applications [38], [39].”
- Easy over hard: a case study on deep learning (2017, methodology): “ where jobs are submi−ed and then analysts wait, wait, and wait for results with “li−le insight into what is really going on behind the scenes, how long it will take, or how much it is going to cost” [20]. Fisher et al. [20] document the issues seen by 16 industrial data scientists, one of whom rema”