New PDF release: Advanced Analytics with Spark: Patterns for Learning from

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By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

ISBN-10: 1491912766

ISBN-13: 9781491912768

During this functional booklet, 4 Cloudera info scientists current a collection of self-contained styles for acting large-scale info research with Spark. The authors carry Spark, statistical equipment, and real-world info units jointly to coach you ways to method analytics difficulties through example.

You'll begin with an advent to Spark and its atmosphere, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields akin to genomics, safety, and finance. in case you have an entry-level realizing of laptop studying and records, and also you software in Java, Python, or Scala, you'll locate those styles beneficial for engaged on your individual info applications.

Patterns include:
- Recommending track and the Audioscrobbler facts set
- Predicting woodland disguise with choice trees
- Anomaly detection in community site visitors with K-means clustering
- realizing Wikipedia with Latent Semantic Analysis
- interpreting co-occurrence networks with GraphX
- Geospatial and temporal information research at the ny urban Taxi journeys data
- Estimating monetary threat via Monte Carlo simulation
- examining genomics facts and the BDG project
- examining neuroimaging information with PySpark and Thunder

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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Sample text

The budget that a type w household is willing to allocate for the purchase of a type k house; the cost of having a type k house in zone i for a type w household; the area (number of acres) used by a type w household with a type k house; the area of available residential land in zone i; the number of type w households to be located. The budget bkw covers the house itself, the size of the lot, the level of amenity and associated trip costs. The cost of housing, p~w, does not reflect the variation of cost with respect to different sites within a zone but it includes the cost 34 CHAPTER 2 attributable to all other characteristics such as the cost of all trips made by the household.

2 TRANSPORTATION PLANNING A traditional transportation planning process consists of four major components: trip generation, trip distribution, modal split, and trip assignment. Many transportation planning problems can be formulated as a linearly constrained convex programming problem whose objective function consists of entropy functions and other cost-related terms [4, 10, 11,25, 26, 34, 43, 44]. This section illustrates the usefulness of entropy optimization models in transportation planning with several well-known trip distribution models.

To address this issue, let us assume that every household has only one key worker and the worker works in a particular zone j. In this way, Tikw can be refined by Ti~w, the number of workers from a type w household living in a type k house in zone i and working in zone j. Similarly, both b~w and p~w can be work-location dependent and refined by b~t and p~t. In other words, both the residential budget and the cost of a house to a particular household type may depend on the work location of the key worker in the household.

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Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills

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