A big data problem involving stick-slip oscillations in an electromechanical system

This chapter explores the role of intelligence in strategy. It first explains what intelligence is and how strategists have talked about its utility before discussing the development of U.S. intelligence in its early efforts to support cold war strategies of containment and deterrence and in its more recent support to strategies for counterterrorism and counterinsurgency. It then examines the challenges and causes of ‘strategic surprise’, focusing on the historical cases of Pearl Harbor in 1941, the Cuban Missile Crisis in 1962, the Yom Kippur War in 1973, and the 11 September 2001 attacks. It also describes some of the new challenges faced by intelligence after a decade of war in Iraq and Afghanistan as well as in dealing with the new ‘big data’ problem.

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Integrating Big Data Services Into an Undergraduate MIS Curriculum

International Journal of Systems and Service-Oriented Engineering ◽

10.4018/ijssoe.2017040104 ◽

2017 ◽

Vol 7 (2) ◽

pp. 58-73 ◽

Cited By ~ 1

Author(s):

Scott Jensen

Keyword(s):

Big Data ◽

Real World ◽

Data Science ◽

Graduate Programs ◽

Advanced Degree ◽

Data Services ◽

Hands On ◽

Data Problem

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist's work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper, the authors present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real-world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science.

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Fast Transient Detection as a Prototypical “Big Data” Problem

Proceedings of the International Astronomical Union ◽

10.1017/s1743921312000993 ◽

2011 ◽

Vol 7 (S285) ◽

pp. 340-341

Author(s):

Dayton L. Jones ◽

Kiri Wagstaff ◽

David Thompson ◽

Larry D'Addario ◽

Robert Navarro ◽

...

Keyword(s):

Big Data ◽

Large Scale ◽

High Time Resolution ◽

Data Generation ◽

Time Data ◽

Transient Detection ◽

Data Archiving ◽

Large Scale Data ◽

Data Problem ◽

Scale Data

AbstractThe detection of fast (< 1 second) transient signals requires a challenging balance between the need to examine vast quantities of high time-resolution data and the impracticality of storing all the data for later analysis. This is the epitome of a “big data” issue—far more data will be produced by next generation-astronomy facilities than can be analyzed, distributed, or archived using traditional methods. JPL is developing technologies to deal with “big data” problems from initial data generation through real-time data triage algorithms to large-scale data archiving and mining. Although most current work is focused on the needs of large radio arrays, the technologies involved are widely applicable in other areas.

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Cancer's Big Data Problem

Computing in Science & Engineering ◽

10.1109/mcse.2017.23 ◽

2017 ◽

Vol 19 (2) ◽

pp. 79-81

Author(s):

Justin H.S. Breaux

Keyword(s):

Big Data ◽

Data Problem

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Research and Application of Massive Data Processing in Oil Services

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.1036 ◽

2012 ◽

Vol 6-7 ◽

pp. 1036-1040

Author(s):

Bao An Li

Keyword(s):

Cloud Computing ◽

Big Data ◽

Internet Of Things ◽

Data Processing ◽

Rapid Growth ◽

Massive Data ◽

Oil Drilling ◽

Data Problem ◽

Traditional Industry ◽

Massive Data Processing

Big data problem has caused widespread concern from industry to academia in recent years. As the amount of data produced by various industries and sectors of rapid growth, increasing demands on data processing and analysis capabilities, how to face the challenges of data, discover new opportunities, the issue has received wide attention. As a traditional industry, the oil drilling or refinery enterprise is facing the operational status of the system to produce large amounts of data. This text introduced an approach to massive data processing for oil enterprise based on cloud computing and Internet of Things.

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