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Chapter 12 In big data, we can't load our data into memory, In many ways, this is a good definition for whether a problem is big data or not—if the data can fit in the memory on your computer, you aren't dealing with a big data problem. Application scenario and goals There are many use cases for big data, in the public and private sectors. The most common experience people have using a big-data-based system is in Internet search, such as Google. To run these systems, a search needs to be carried out over billions of websites in a fraction of a second. Doing a basic text-based search would be inadequate to deal with such a problem. Simply storing the text of all those websites is a large problem. In order to deal with queries, new data structures and data mining methods need to be created and implemented specifically for this application. Big data is also used in many other scientific experiments such as the Large Hadron Collider, part of which is pictured below, that stretches over 17 kilometers and contains 150 million sensors monitoring hundreds of millions of particle collisions per second. The data from this experiment is massive, with 25 petabytes created daily, after a filtering process (if filtering was not used, there would be 150 million petabytes per year). Analysis on data this big has led to amazing insights about our universe, but has been a significant engineering and analytics challenge. [ 273 ]
Working with Big Data Governments are increasingly using big data too, to track populations, businesses, and other aspects about their country. Tracking millions of people and billions of interactions (such as business transactions or health spending) has led to a need for big data analytics in many government organizations. Traffic management is a particular focus of many governments around the world, who are tracking traffic using millions of sensors to determine which roads are most congested and predicting the impact of new roads on traffic levels. Large retail organizations are using big data to improve the customer experience and reduce costs. This involves predicting customer demand in order to have the correct level of inventory, upselling customers with products they may like to purchase, and tracking transactions to look for trends, patterns, and potential frauds. Other large businesses are also leveraging big data to automate aspects of their business and improve their offering. This includes leveraging analytics to predict future trends in their sector and track external competitors. Large businesses also use analytics to manage their own employees—tracking employees to look for signs that an employee may leave the company, in order to intervene before they do. The information security sector is also leveraging big data in order to look for malware infections in large networks, by monitoring network traffic. This can include looking for odd traffic patterns, evidence of malware spreading, and other oddities. Advanced Persistent Threats (APTs) is another problem, where a motivated attacker will hide their code within a large network to steal information or cause damage over a long period of time. Finding APTs is often a case of forensically examining many computers, a task which simply takes too long for a human to effectively perform themselves. Analytics helps automate and analyze these forensic images to find infections. Big data is being used in an increasing number of sectors and applications, and this trend is likely to only continue. MapReduce There are a number of concepts to perform data mining and general computation on big data. One of the most popular is the MapReduce model, which can be used for general computation on arbitrarily large datasets. [ 274 ]
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[ 1 ] www.allitebooks.com
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Authorship Attribution This dataset
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Authorship Attribution "instead", "
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Authorship Attribution Support vect
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Authorship Attribution Kernels When
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Authorship Attribution We can reuse
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Authorship Attribution With our dat
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Authorship Attribution We then reco
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Authorship Attribution If it doesn'
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Authorship Attribution Finally, we
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Clustering News Articles In most of
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Chapter 10 API Endpoints are the ac
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The token object is just a dictiona
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Chapter 10 We then create a list to
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Chapter 10 We are going to use MD5
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Chapter 10 Next, we develop the cod
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Page 321 and 322: Next Steps… Extending the IPython
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Page 325 and 326: Next Steps… Vowpal Wabbit http://
Page 327 and 328: Next Steps… Deeper networks These
Page 329 and 330: Next Steps… Real-time clusterings
Page 331 and 332: Next Steps… More resources Kaggle
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Page 337 and 338: NetworkX about 145 defining 303 URL
Page 339 and 340: scikit-learn package references 305
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Page 344: Learning Python Data Visualization
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