The pathologies of big data

By A. Jacobs. Communications of the ACM, 2009

789 citations recorded by the source corpus.

Subfields: Health Information Management, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/fef0acea0e562390ddcbfd2f50900b5a88fb65a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Angappa Gunasekaran
2. Jimmy J. Lin
3. Chris Dyer
4. Ramón Alvarado
5. Philip Chen
6. Chunming Zhang
7. Katherine C. Kellogg
8. Angèle Christin

In Information Systems and Management

1. Angappa Gunasekaran
2. Thanos Papadopoulos
3. Samuel Fosso Wamba
4. Jimmy J. Lin
5. Chris Dyer
6. Rameshwar Dubey
7. Ramón Alvarado
8. Philip Chen

Citations in the corpus

• Big Data: Issues and Challenges Moving Forward (2013, background): “Jacobs [9] summarized the issue very succinctly – “.”
• A big data approach for logistics trajectory discovery from RFID-enabled production data (2015, neutral): “Big Data refers to a data set which collects large and complex data that is hard to process using traditional applications (Jacobs, 2009).”
• Can we trust Big Data? Applying philosophy of science to software (2016, background): “See also Jacobs (2009) for a discussion of the importance of analysis in Big Data.”
• Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks (2014, background): “Big data is one of the fundamental phenomena of the information era of human societies [Jacobs, 2009; Snijders et al., 2012; Wang, 2014a; Wang & Wiebe, 2014].”
• Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, background): “Davenport et al. focused more on the variety aspect of data sources [93], while some other authors emphasized the storage (volume) and analysis parts when it comes to dealing with big data [94,95].”
• Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “There are many critiques and negative opinions [81] [167] [11] from the pessimists.”
• Big data, bigger dilemmas: A critical review (2015, background): “85), and Jacobs (2009) suggests that Big Data refers to “data whose size forces us to look beyond the tried-and-true methods that are prevalent at that time” (p.”
• Big Data Analytics in Healthcare (2015, background): “Various attempts at defining big data essentially characterize it as a collection of data elements whose size, speed, type, and/or complexity require one to seek, adopt, and invent new hardware and software mechanisms in order to successfully store, analyze, and visualize the data [1–3].”
• Data mining with big data (2016, background): “Such combined characteristics suggest that Big Data require a “big mind” to consolidate data for maximum values [27].”
• How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study (2015, neutral): “(Gorton, et al., 2008), (Field, et al., 2009), (Jacobs, 2009), (Bughin, et al., 2010), (Schadt, et al., 2010), (Acker, et al., 2011), (Alexander, et al., 2011), (Bradbury, 2011), (Brown, et al., 2011), (Bughin, et al., 2011), (Long & Siemens, 2011), (Sobek, et al., 2011), (Anderson & Blanke, 2012),…”
• The impact of big data on world-class sustainable manufacturing (2015, background): “others emphasize the importance of storage and analysis [5, 23] highlighting the role of analytics.”
• Algorithms at Work: The New Contested Terrain of Control (2020, methodology): “Second, algorithms now typically provide instantaneous feedback , which relates to the velocity aspect of big data (Jacobs, 2009; Katal, Wazid, & Goudar, 2013).”
• Digital Finance and FinTech: current research and future research directions (2017, neutral): “Therefore, big data is defined as ‘‘data whose size forces us to look beyond the tried-and-true methods that are prevalent at that time’’ (Jacobs 2009) and is specifically characterized by an enormous volume of data to be analyzed, high processing velocity, and a variety of data sources to be taken…”
• Data colonialism through accumulation by dispossession: New metaphors for daily data (2015, neutral): “This definition echoes Jacobs (2009) conclusion that the “pathologies of big data are primarily those of analysis”(Jacobs, 2009, page 4).”
• Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “11For more detail, Jacobs [76] provides real-world benchmarks in his discussion of largedata problems.”