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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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The awesome points to learn from DB2 NoSQL GraphStore

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The awesome points to learn from db2 graphstore
 #db2 graphstore:
One best example, prior to understanding the RDF format for Graph data model- If the graph data model is the model the semantic web uses to store data, RDF is the format in which it is written. 


Summary of DB2 Graph Store:
  • DB2-RDF support is officially called "NoSQL Graph Support".  
  • The API extends the Jena API (Graph layer).  Developers familiar with Jena TDB will have the Model layer capabilities they are accustomed to.
  • Although the DB2-RDF functionality is being released with DB2 LUW 10.1, it is also compatible with DB2 9.7.
  • Full supports for SPARQL 1.0 and a subset of SPARQL 1.1.  Full SPARQL 1.1 support (which is till a W3C working draft) will be forthcoming.
  • While RDBMS implementations of RDF graphs have typically been non-performant, that is not the case here*.  Some very impressive and innovative work has been put into optimization capabilities.  Out-of-the box performance is comparable with native triple stores, and read/write performance in the optimized schema has been seen to surpass these speeds.
Related: Presentation on DB2 NoSQL Graph Store

What is RDF data model(ref:wiki)

The RDF data model is similar to classical conceptual modeling approaches such as entity–relationship or class diagrams, as it is based upon the idea of making statements about resources (in particular web resources) in the form of subject–predicate–object expressions.  

 These expressions are known as triples in RDF terminology. The subject denotes the resource, and the predicate denotes traits or aspects of the resource and expresses a relationship between the subject and the object. For example, one way to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has", and an object denoting "the color blue". Therefore, RDF swaps object for subject that would be used in the classical notation of an entity–attribute–value model within object-oriented design; Entity (sky), attribute (color) and value (blue). RDF is an abstract model with several serialization formats (i.e., file formats), and so the particular way in which a resource or triple is encoded varies from format to format. 

 This mechanism for describing resources is a major component in the W3C's Semantic Web activity: an evolutionary stage of the World Wide Web in which automated software can store, exchange, and use machine-readable information distributed throughout the Web, in turn enabling users to deal with the information with greater efficiency and certainty. 
RDF's simple data model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. 
A collection of RDF statements intrinsically represents a labeled, directed multi-graph. As such, an RDF-based data model is more naturally suited to certain kinds of knowledge representation than the relational model and other ontological models. However, in practice, RDF data is often persisted in relational database or native representations also called Triplestores, or Quad stores if context (i.e. the named graph) is also persisted for each RDF triple.[3] ShEX, or Shape Expressions,[4] is a language for expressing constraints on RDF graphs. It includes the cardinality constraints from OSLC Resource Shapes and Dublin Core Description Set Profiles as well as logical connectives for disjunction and polymorphism. As RDFS and OWL demonstrate, one can build additional ontology languages upon RDF.

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