By Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills
During this functional publication, 4 Cloudera information scientists current a suite of self-contained styles for acting large-scale information research with Spark. The authors carry Spark, statistical tools, and real-world facts units jointly to educate you the way to process analytics difficulties through example.
You'll commence with an creation to Spark and its environment, after which dive into styles that observe universal techniques—classification, collaborative filtering, and anomaly detection between others—to fields resembling genomics, protection, and finance. when you have an entry-level figuring out of computer studying and records, and also you software in Java, Python, or Scala, you'll locate those styles invaluable for engaged on your personal facts applications.
- Recommending tune and the Audioscrobbler facts set
- Predicting wooded area disguise with selection trees
- Anomaly detection in community site visitors with K-means clustering
- realizing Wikipedia with Latent Semantic Analysis
- reading co-occurrence networks with GraphX
- Geospatial and temporal information research at the big apple urban Taxi journeys data
- Estimating monetary chance via Monte Carlo simulation
- studying genomics info and the BDG project
- reading neuroimaging information with PySpark and Thunder
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Additional resources for Advanced Analytics with Spark: Patterns for Learning from Data at Scale
2 Performance of Simulation The performance of the simulation has been measured for the simulation of fourwheeled vehicles in a simpliﬁed urban scenario described above. Measurements were taken on two laptop computers (cf. Table 1) equipped with a single resp. dual core CPU. Fig. 3. Structure of the simulation: Vehicle data, motion and laser scanner (LS) simulation and the controllers can be duplicated to simulate more than one vehicle. Arrows indicate direction of data ﬂow. 38 M. Friedmann, K.
On close loop stability for cooperative control. On the other hand, distributed 44 M. Kropﬀ et al. control may change the network topology, improving routing eﬃciency or covering a wider area while remaining connected. This combination is obviously bidirectional and very important with respect to cooperative control of robotic groups. Typically, ﬁeld data is provided by sensors. Cooperative data gathering based on aggregated information is closely related to the positions of the robots and viewing angles of their sensors.
Virtual Reality Toolbox is used to present in soft real-time the state of each vehicle involved in the mission. A VRML world can be customized in terms of textures, position of camera(s) (attached to vehicle or ﬁxed), light(s). The above mentioned toolbox is also used to interface a joystick; this kind of device allows a manual control of the helicopter (user can select the set of axes that wants to control). This features is really useful for novel pilot(s) during the training phases. A 3D model of Bergen Twin Observer Helicopter was developed; a more 24 A.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale by Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills