Merge branch 'release/0.1.0'

Author: Aklakan

.gitignore

@@ -13,6 +13,8 @@ buildNumber.properties
 .preferences
 .project
 
+deptree.txt
+
 ### Java template
 *.class
 

README.md

@@ -1,39 +1,36 @@
-Quick start:
-* Import the project into your IDE of choice
-* Run the class [MainS2RdfExample.scala](s2rdf-example/src/main/scala/org/aksw/s2rdf/example/MainS2RdfExample.scala)
+# SANSA Query
 
+## Description
+SANSA Query is a library to perform queries directly into [Spark](https://spark.apache.org) or [Flink](https://flink.apache.org). It allows files to reside in HDFS as well as in a local file system and distributes executions across Spark RDDs/DataFrames or Flink DataSets.
 
+SANSA uses vertical partitioning (VP) approach and is designed to support extensible partitioning of RDF data. Instead of dealing with a single three-column table (s, p, o), data is partitioned into multiple tables based on the used RDF predicates, RDF term types and literal datatypes. The first column of these tables is always a string representing the subject. The second column always represents the literal value as a Scala/Java datatype. Tables for storing literals with language tags have an additional third string column for the language tag. Its uses [Sparqlify](https://github.com/AKSW/Sparqlify) as a scalable SPARQL-SQL rewriter.
 
-== dataset-creator
-vp = vertical partitioning -> each predicate to its own table
+### SANSA Query Spark
+On SANSA Query Spark the method for partitioning a RDD[Triple] is located in [RdfPartitionUtilsSpark](https://github.com/SANSA-Stack/SANSA-RDF/blob/develop/src/main/scala/net/sansa_stack/rdf/spark/partition/core/RdfPartitionUtilsSpark.scala). It uses an [RdfPartitioner](https://github.com/SANSA-Stack/SANSA-RDF/blob/develop/src/main/scala/net/sansa_stack/rdf/common/partition/core/RdfPartitioner.scala) which maps a Triple to a single [RdfPartition](https://github.com/SANSA-Stack/SANSA-RDF/blob/develop/src/main/scala/net/sansa_stack/rdf/common/partition/core/RdfPartition.scala) instance.
 
-  runDriver with 3 args:
-1: 'working directory: where the indexes are placed; ./VP ./ExtVP'
-2: 'inFile - file within the working directory'
-3: 'joinType: VP, ss, so, os' (VP must be used first; ss, so and os are then generated under expVP)
-4: 'some threshold of when to not create the extVP index - 1 which always creates all extVP; lorenz thinks it was 0.7 in the authors scripts'
+* RdfPartition, as the name suggests, represents a partition of the RDF data and defines two methods:
+matches(Triple): Boolean: This method is used to test whether a triple fits into a partition.
+  * Layout => TripleLayout: This method returns the [TripleLayout](https://github.com/SANSA-Stack/SANSA-RDF/blob/develop/src/main/scala/net/sansa_stack/rdf/common/partition/layout/TripleLayout.scala) associated with the partition, as explained below.
+  * urthermore,RdfPartitions are expected to be serializable, and to define equals and hash code.
+* TripleLayout instances are used to obtain framework-agnostic compact tabular representations of triples according to a partition. For this purpose it defines the two methods:
+  * fromTriple(triple:Triple): Product: This method must, for a given triple, return its representation as a Product(this is the super class of all scalaTuples)
+  * schema:Type: This method must return the exact scala type of the objects returned by fromTriple, such as typeOf[Tuple2[String,Double]]. Hence, layouts are expected to only yield instances of one specific type.
 
+See the [available layouts](https://github.com/SANSA-Stack/SANSA-RDF/blob/develop/src/main/scala/net/sansa_stack/rdf/common/partition/layout) for details.
 
-Settings
-currently several settings hard coded
+## Usage
 
+The following Scala code shows how to query an RDF file SPQRQL syntax (be it a local file or a file residing in HDFS):
+```scala
 
-spark reads default properties from
-src/main/resources/conf/spark-default.properties
-
-
-== query translation
-query-translator.run.Main 
-
-Generates sparql sql - run the main method to see what SQL gets generated
-
-
-
-== query execution
-runDriver
-1: dbDir: base directory
-2: qrFile: Query plan file name - gets generated by the query translator (don't write it manually)
-Uses some strange format to create a list queries
-QueryTranslator/QYagoQUerySet/sql_090/
+val graphRdd = NTripleReader.load(sparkSession, new File("path/to/rdf.nt"))
+ 
+val partitions = RdfPartitionUtilsSpark.partitionGraph(graphRdd)
+val rewriter = SparqlifyUtils3.createSparqlSqlRewriter(sparkSession, partitions)
+ 
+val qef = new QueryExecutionFactorySparqlifySpark(sparkSession, rewriter)
+```
+An overview is given in the [FAQ section of the SANSA project page](http://sansa-stack.net/faq/#sparql-queries). Further documentation about the builder objects can also be found on the [ScalaDoc page](http://sansa-stack.net/scaladocs/).
 
+