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To begin does your code, software, or tool carry out a data conversion or a data extraction?  If a conversion the tool should be included in the Data Access Proxy.  If an extraction the tool should be included in the Data Tilling Service.

Anchor
DAP
DAP
The Data Access Proxy (DAP)

The Data Access Proxy handles data conversions.  If a piece of software or tool exists to carry out the conversion its incorporation into the DAP will be through Polyglot.  If the specification of the file format is known then in can be incorporated as a DFDL schema within Daffodil.

Anchor
Polyglot
Polyglot
Polyglot Software Server Scripts

...

Code Block
titlepgm.dfdl.xsd
<?xml version="1.0" encoding="UTF-8"?>

<!--
Load image data from a PGM file and represent the data as a sequence of pixels in row major order.
-->

<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dfdl="http://www.ogf.org/dfdl/dfdl-1.0/" xmlns:ex="http://example.com" targetNamespace="http://example.com">
  <xs:include schemaLocation="xsd/built-in-formats.xsd"/>

  <xs:annotation>
    <xs:appinfo source="http://www.ogf.org/dfdl/">
      <dfdl:format ref="ex:daffodilTest1" separator="" initiator="" terminator="" leadingSkip='0' textTrimKind="none" initiatedContent="no"
        alignment="implicit" alignmentUnits="bits" trailingSkip="0" ignoreCase="no" separatorPolicy="suppressed" 
        separatorPosition="infix" occursCountKind="parsed" emptyValueDelimiterPolicy="both" representation="text" 
        textNumberRep="standard" lengthKind="delimited" encoding="ASCII"/>
    </xs:appinfo>
  </xs:annotation>

  <xs:element name="file">
    <xs:complexType>
      <xs:sequence>

        <xs:element name="header" dfdl:lengthKind="implicit" maxOccurs="1">
          <xs:complexType>
            <xs:sequence dfdl:sequenceKind="ordered" dfdl:separator="%NL;" dfdl:separatorPosition="postfix">
              <xs:element name="type" type="xs:string"/>
              <xs:element name="dimensions" maxOccurs="1" dfdl:occursCountKind="implicit">
                <xs:complexType>
                  <xs:sequence dfdl:sequenceKind="ordered" dfdl:separator="%SP;">
                    <xs:element name="width" type="xs:integer"/>
                    <xs:element name="height" type="xs:integer"/>
                  </xs:sequence>
                </xs:complexType>
              </xs:element>
              <xs:element name="depth" type="xs:integer"/>
            </xs:sequence>
          </xs:complexType>
        </xs:element>

        <xs:element name="pixels" dfdl:lengthKind="implicit" maxOccurs="1">
          <xs:complexType>
            <xs:sequence dfdl:separator="%SP; %NL; %SP;%NL;" dfdl:separatorPosition="postfix" dfdl:separatorSuppressionPolicy="anyEmpty">
              <xs:element name="pixel" type="xs:integer" maxOccurs="unbounded" dfdl:occursCountKind="expression"
                dfdl:occursCount="{../../ex:header/ex:dimensions/ex:width * ../../ex:header/ex:dimensions/ex:height }"/>
            </xs:sequence>
          </xs:complexType>
        </xs:element>

      </xs:sequence>
    </xs:complexType>
  </xs:element>

</xs:schema>

...

The Data Tilling Services handles data extractions.  If your code, tool, or software extracts information such as keywords from a file or its contents then it should be included in the DTS as a Medici extractor.  If your code, tool, or software extracts a signature from the file's contents which in turn can be compared to the signatures of other files via some distance measure to find similar pieces of data, then, it should be included in the DTS as a Versus extractor.

Anchor
Medici
Medici
Medici Extractors

Medici extractors typically serve to automatically extract some new kind of information from a file's content when it is uploaded into Medici.  These extractors do this by connecting to a shared RabbitMQ bus.  When a new file is uploaded to Medici it is announced on this bus.  Extractors that can handle a file of the type posted on the bus are triggered and the data they in turn create is returned to Medici as derived data to be associated with that file.  The extractors themselves can be implemented in a variety of languages. Examples of these extractors in different languages can be found in the extractors-templates code repository.

Anchor
Java
Java
Java

An extractor must establish a connection with the Medici RabbitMQ bus, handle incoming messages, start jobs based on received messages, and ultimatley carry out a job on a given file.  The example below simply counts the number of words in a document and returns this information as a piece of metadata to be associated with the file.

Code Block
themeEmacs
languagejava
titleConnecting to RabbitMQ
protected void startExtractor(String rabbitMQUsername, String rabbitMQpassword)
{
	try{ 
 		//Open channel and declare exchange and consumer
		ConnectionFactory factory = new ConnectionFactory();
		factory.setHost(serverAddr);
		factory.setUsername(rabbitMQUsername);
		factory.setPassword(rabbitMQpassword);
		Connection connection = factory.newConnection();

 		final Channel channel = connection.createChannel();
		channel.exchangeDeclare(EXCHANGE_NAME, "topic", true);

		channel.queueDeclare(QUEUE_NAME,DURABLE,EXCLUSIVE,AUTO_DELETE,null);
		channel.queueBind(QUEUE_NAME, EXCHANGE_NAME, "*.file.text.plain.#");
 
 		this.channel = channel;

 		// create listener
		channel.basicConsume(QUEUE_NAME, false, CONSUMER_TAG, new DefaultConsumer(channel) {
 			@Override
 			public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
				messageReceived = new String(body);
 				long deliveryTag = envelope.getDeliveryTag();
 				// (process the message components here ...)
				System.out.println(" {x} Received '" + messageReceived + "'");
 
				replyProps = new AMQP.BasicProperties.Builder().correlationId(properties.getCorrelationId()).build();
				replyTo = properties.getReplyTo();
 
				processMessageReceived();
				System.out.println(" [x] Done");
				channel.basicAck(deliveryTag, false);
			}
		});

 		// start listening 
		System.out.println(" [*] Waiting for messages. To exit press CTRL+C");

  		while (true) {
			Thread.sleep(1000);
		}
	}catch(Exception e){
		e.printStackTrace();
		System.exit(1);
	} 
}

Java extractors can be created using the amqp-client jar file. This allows you to connect to the RabitMQ bus and received messages. The easiest way to get up and running is to use maven with java to add all required dependencies. An example of an extractor written in java can be found at Medici Extractor in Java.

Anchor
Python
Python
Python

Python extractors will often be based on the packages pika and requests. This allows you to connect to the RabittMQ message bus and easily send requests to medici. A complete example of the python extractor can be found at Medici Extractor in Python

Calling R Scripts from Python

Coming soon...

Anchor
Versus
Versus
Versus Extractors

Versus extractors serve to extract a signature from a file's content.  These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure.  Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure.  The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

Anchor
Java Measure
Java Measure
Java

The main class sets up the comparison, this is done by adding the two files that need to be compared, as well as the adapter to load the file, the extractor to extract a feature from the file, and a measurement to compare the two features.

Code Block
languagejava
titleMain
    static public void main(String[] args) {         
        PairwiseComparison comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.txt"));
        comparison.setSecondDataset(new File("data/test2.txt"));
        comparison.setAdapterId(TextAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());

        ExecutionEngine ee = new ExecutionEngine(); 
        ee.submit(comparison, new ComparisonStatusHandler() {
            @Override
            public void onStarted()
Code Block
languagejava
titleProcessing Messages
protected void processMessageReceived()
{
  try {
    try {
      ExampleJavaExtractorService extrServ = new ExampleJavaExtractorService(this);
      jobReceived = getRepresentation(messageReceived, ExtractionJob.class);System.out.println("STARTED : ");
        
    }

         File textFile = extrServ.processJob(jobReceived);
 @Override
        jobReceived.setFlag("wasText");    public void onFailed(String msg, Throwable e) {
      log.info("Word count extraction complete. Returning word count file as intermediate resultSystem.out.println("FAILED  : " + msg);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Word count extraction complete. Returning word count file as intermediate result.", log          e.printStackTrace();
      uploadIntermediate(textFile, "text/plain", log);
          textFileSystem.deleteexit(0);
        
    }

  sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
    } catch (Exception ioe   @Override
            public void onDone(double value) {
              log.error  System.out.println("CouldDONE  not finish extraction: job.", + ioevalue);
       sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Could not finish extraction job.", log         System.exit(0);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
      }

       }
  } catch(Exception e) {@Override
      e.printStackTrace();
      public void System.exit(1);onAborted(String msg) {
  } 
}
Code Block
languagejava
titleProcessing Jobs
public File processJob(ExtractionJob receivedMsg) throws Exception 
{    
  log.info("Downloading text file with ID "+ receivedMsg.getIntermediateId() +" from " + receivedMsg.getHost());
  callingExtractor.sendStatus(receivedMsg.getId(), callingExtractor.getClass().getSimpleName(), "Downloading text file.", log);
      
  DefaultHttpClient httpclient = new DefaultHttpClient( System.out.println("ABORTED : " + msg);
                System.exit(0);
            }
        });
  HttpGet httpGet = new HttpGet(receivedMsg.getHost() +"api/files/"+ receivedMsg.getIntermediateId()+"?key="+playserverKey);    
  HttpResponse fileResponse = httpclient.execute(httpGet);
  log.info(fileResponse.getStatusLine());

  if(fileResponse.getStatusLine().toString().indexOf("200") == -1){
    throw new IOException("File not found.");
  }

  HttpEntity fileEntity = fileResponse.getEntity();
  InputStream fileIs = fileEntity.getContent();
  
  Header[] hdrs = fileResponse.getHeaders("content-disposition");
  String contentDisp = hdrs[0].toString();
  
  String fileName = contentDisp.substring(contentDisp.indexOf("filename=")+9);
  File tempFile = File.createTempFile(fileName.substring(0, fileName.lastIndexOf(".")),      fileName.substring(fileName.lastIndexOf(".")).toLowerCase());
  OutputStream fileOs = new FileOutputStream(tempFile);   
  IOUtils.copy(fileIs,fileOs);
  fileIs.close();
  fileOs.close }

The text adapter will take a text file, and load all the file, splitting the text into words and return a list of all words in the text. The words are still in the right order, and it is possible to read the original information of the file by reading the words in the order as they are returned by getWords().

Code Block
languagejava
titleText Adapter
public class TextAdapter implements FileLoader, HasText {
    private File         file;
    private List<String> words;

    public TextAdapter() {}

    // ----------------------------------------------------------------------
    // FileLoader
    // ----------------------------------------------------------------------
    @Override
    public void load(File file) {
        this.file = file;
    }

    @Override
    public String getName() {
        return "Text Document";
    }

    @Override
    public List<String> getSupportedMediaTypes() {
        List<String> mediaTypes = new ArrayList<String>();
      
  EntityUtilsmediaTypes.consume(fileEntityadd("text/*");
 
      
  log.info("Download complete. Initiating word count generation");
  return mediaTypes;
  File textFile = processFile(tempFile, receivedMsg.getId());  
  return textFile;       
}
Code Block
languagejava
titleProcessing Files
private File processFile(File tempFile, String originalFileId) throws Exception
{
    Runtime r = Runtime.getRuntime();// ----------------------------------------------------------------------
    Process p;// HasText
     // Process tracks one external native process
      
    String tempDir = System.getProperty("java.io.tmpdir");

	if (new Character(tempDir.charAt(tempDir.length()-1)).toString().equals(System.getProperty("file.separator")) == false){
// ----------------------------------------------------------------------
    @Override
    public List<String> getWords() {
        if (words == null) {
            tempDirwords = tempDir + System.getProperty("file.separator"new ArrayList<String>();
      }
      try {
     String  processCmd = "";
    String operSystem = System.getProperty("os.name").toLowerCase();

	// TODO: windows impl
    if(operSystem.indexOf("nix") >= 0 || operSystem.indexOf("nux") >= 0 || operSystem.indexOf("aix") > 0 ){ BufferedReader br = new BufferedReader(new FileReader(file));
                String line;
      "wc -w " + tempDir +  tempFile.getName();
    }  while((line = br.readLine()) != null) {
      
     p = r.exec(processCmd, null, new File(tempDir));
    StreamGobblerString[] outputGobblerw = new StreamGobbler(p.getInputStream(), "INFO", log);
line.split(" ");
                StreamGobbler errorGobbler = new StreamGobblerwords.addAll(pArrays.getErrorStreamasList(w),"ERROR", log);
    outputGobbler.start();
    errorGobbler.start();
    p.waitFor();
      }
    File outFile = new File(tempDir + tempFile.getName().substring(0, tempFile.getName().lastIndexOf(".")) + ".txt");
    tempFilebr.deleteclose();

	if(!Files.exists(outFile.toPath()))
      throw new Exception("File not processed correctly. File} iscatch possibly corrupt.");
(IOException e) {
           
  	return outFile;  
}

...

Code Block
languagecpp
titleConnecting to RabbitMQ
#include <amqpcpp.h>

namespace CPPExample
{
  class RabbitMQConnectionHandler : public AMQP::ConnectionHandler { e.printStackTrace();
            }
      /**  }
      *  return words;
    }
 }

The extractor will take the words returned by the adapter and count the occurrence of each word. At this point we are left with a histogram with all words and how often they occur in the text, we can no longer read the text since the information about the order of the words is lost.

Code Block
languagejava
titleText Histogram Extractor
public class TextHistogramExtractor implements Extractor
{
    @OverrideMethod that is called by the AMQP library every time it has data
    public Adapter *newAdapter() {
 available that should be sent to RabbitMQ. 
throw (new RuntimeException("Not supported."));
   * }

 @param  connection @Override
 pointer to the mainpublic connectionString objectgetName()  {
      *  @paramreturn "Text dataHistogram Extractor";
    }

   memory buffer@Override
 with the data thatpublic shouldSet<Class<? beextends sent to RabbitMQAdapter>> supportedAdapters() {
      *  @paramSet<Class<? extends sizeAdapter>> adapters = new HashSet<Class<? extends Adapter>>();
  size of the buffer
      */adapters.add(HasText.class);
     virtual void onData(AMQP::Connection *connection, const char *data, size_t size) return adapters;
    }

     {@Override
    public Class<? extends Descriptor>  // @todo 
getFeatureType() {
        return LabelHistogramDescriptor.class;
  //  }

 Add your own implementation,@Override
 for example by doingpublic aDescriptor call to the
  extract(Adapter adapter) throws Exception {
       // if send()adapter systeminstanceof call. But be aware that the send() call may not
HasText) {
            LabelHistogramDescriptor desc = new  //LabelHistogramDescriptor();

   send all data at once, so you also need for to(String takeword care of buffering
: ((HasText) adapter).getWords()) {
           //  the bytes that could not immediately be sent, and try to send 
    desc.increaseBin(word);
            }

      //  them again when the return socketdesc;
 becomes writable again
     } else {

      /**
      *throw new MethodUnsupportedTypeException();
 that is called by the AMQP library when}
 the login attempt }
    
  *  succeeded.@Override
 After this method haspublic been called, the connection is ready boolean hasPreview(){
      *  toreturn use.false;
      *}
  @param  connection      The connection that can now be used
      */@Override
    public String virtual void onConnected(Connection *connection)
      previewName(){
         // @todoreturn null;
    }
}

To compare two texts we use the euclidian distance measure of two histograms. First we normalize each histogram, so we can compare a large text with a small text, next we compare each big of the two histograms. If the bin is missing from either histogram it is assumed to have a value of 0.

Code Block
languagejava
titleEuclidian Distance Measure
public class LabelHistogramEuclidianDistanceMeasure implements Measure 
{
    @Override
    public SimilarityPercentage normalize(Similarity similarity) {
     //  add your own implementation, for example by creating a channel 
         //  instance, andreturn startnew publishingSimilarityPercentage(1 or consuming- similarity.getValue());
      }

      /**@Override
    public String *getFeatureType() {
 Method that is called by the AMQP library when a fatal error occursreturn LabelHistogramDescriptor.class.getName();
    }

    @Override
  *  onpublic the connection, for example because data received from RabbitMQ
      *  could not be recognized.String getName() {
        return "Histogram Distance";
    }

    @Override
    public Class<LabelHistogramEuclidianDistanceMeasure> *getType() {
 @param  connection     return TheLabelHistogramEuclidianDistanceMeasure.class;
 connection on which the error occured }

    // correlation

 *  @param @Override
 message   public Similarity compare(Descriptor desc1, Descriptor desc2) Athrows human readable error messageException {
      */
  if ((desc1 instanceof LabelHistogramDescriptor) virtual&& void onError(Connectiondesc2 *connection, const std::string &message)
instanceof LabelHistogramDescriptor)) {
       {
     LabelHistogramDescriptor lhd1 = // @todo(LabelHistogramDescriptor) desc1;
        //   add yourLabelHistogramDescriptor ownlhd2 implementation, for example by reporting the error
= (LabelHistogramDescriptor) desc2;

            // get toall thepossible userlabels
 of your program, log the error, and destruct the 
  Set<String> labels = new HashSet<String>();
  //  connection object because it is no longer in a usable state
 labels.addAll(lhd1.getLabels());
        }
  };

}
Code Block
languagecpp
titleReceiver
namespace CPPExample 
{ labels.addAll(lhd2.getLabels());

  /**
   *  Parse data that was recevied from// RabbitMQnormalize
   *  
   *  Every time that data comes in from RabbitMQ, you should call this method to parse lhd1.normalize();
            lhd2.normalize();
   *  the incoming data, and let it handle by
 the AMQP-CPP library. This method returns the number
   * // ofcompute bytesdistance
 that were processed.
   *
   *  If notdouble allsum bytes could be processed because it only contained a partial frame, you should
   *  call this same method later on when more data is available. The AMQP-CPP library does not do
   *  any buffering, so it is up to the caller to ensure that the old data is also passed in that
   *  later call.
   *
   *  @param  buffer= 0;

            for (String s : labels) {
                Double b1 = lhd1.getBin(s);
                Double b2 = lhd2.getBin(s);
             
         buffer to decode
   *  if @param(b1 == sizenull) {
       size of the buffer to decode
   *  @return   sum += b2 * b2;
        number of bytes that were processed
   */
} else size_tif parse(char *buffer, size_t size)
  {
b2 == null) {
                    sum return _implementation.parse(buffer, size);
  }
}

...

Code Block
themeEmacs
languagepy
titleInstantiating the logger and starting the extractor
def main():
 global logger

  # name of receiver
  receiver='ExamplePythonExtractor'

  # configure the logging system
  logging.basicConfig(format="%(asctime)-15s %(name)-10s %(levelname)-7s : %(message)s", level=logging.WARN)
  logger = logging.getLogger(receiver)
  logger.setLevel(logging.DEBUG)
 
  if len(sys.argv) != 4:
    logger.info("Input RabbitMQ username, followed by RabbitMQ password and Medici REST API key.")
    sys.exit()
 
  global playserverKey
  playserverKey = sys.argv[3]
  global exchange_name
  exchange_name = sys.argv[4]
Code Block
themeEmacs
languagepy
titleConnecting to RabbitMQ
# connect to rabbitmq using input username and password 
credentials = pika.PlainCredentials(sys.argv[1], sys.argv[2])
parameters = pika.ConnectionParameters(credentials=credentials)
connection = pika.BlockingConnection(parameters)
 
 # connect to channel
channel = connection.channel()

 # declare the exchange
channel.exchange_declare(exchange='medici', exchange_type='topic', durable=True)

 # declare the queue
channel.queue_declare(queue=receiver, durable=True)

 # connect queue and exchange
channel.queue_bind(queue=receiver, exchange='medici', routing_key='*.file.text.plain')

 # create listener
channel.basic_consume(on_message, queue=receiver, no_ack=False)

 # start listening
logger.info("Waiting for messages. To exit press CTRL+C")
 try:
   channel.start_consuming()
 except KeyboardInterrupt:
   channel.stop_consuming()

# close connection
connection.close()
Code Block
languagepy
titleProcessing Messages
def on_message(channel, method, header, body):
	global logger
	statusreport = {}
	inputfile=None
	try:
		# parse body back from json
		jbody=json.loads(body)

		host=jbody['host']
		fileid=jbody['id']
		intermediatefileid=jbody['intermediateId']
		if not (host.endswith('/')):
			host += '/'
		
		# for status reports
		statusreport['file_id'] = fileid
		statusreport['extractor_id'] = 'wordCount' 

		# print what we are doing
		logger.debug("[%s] started processing", fileid)

		# fetch data
		statusreport['status'] = 'Downloading file.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport)) 
		url=host + 'api/files/' + intermediatefileid + '?key=' + playserverKey
		r=requests.get(url, stream=True)
		r.raise_for_status()
		(fd, inputfile)=tempfile.mkstemp()
		with os.fdopen(fd, "w") as f:
			for chunk in r.iter_content(chunk_size=10*1024):
				f.write(chunk)

		# create word count
		statusreport['status'] = 'Creating word count.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport))
		create_word_count(inputfile, ext, host, fileid)

		# Ack
		channel.basic_ack(method.delivery_tag)
		logger.debug("[%s] finished processing", fileid)
	except subprocess.CalledProcessError as e:
		logger.exception("[%s] error processing [exit code=%d]\n%s", fileid, e.returncode, e.output)
		statusreport['status'] = 'Error processing.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S') 
		channel.basic_publish(exchange='',
                routing_key=header.reply_to,
                properties=pika.BasicProperties(correlation_id = \
                                                header.correlation_id),
                body=json.dumps(statusreport)) 
	except:
		logger.exception("[%s] error processing", fileid)
		statusreport['status'] = 'Error processing.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S') 
		channel.basic_publish(exchange='',
                routing_key=header.reply_to,
                properties=pika.BasicProperties(correlation_id = \
                                                header.correlation_id),
                body=json.dumps(statusreport))		
	finally:
		statusreport['status'] = 'DONE.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport))
		if inputfile is not None:
			try:
				os.remove(inputfile)
			except OSError:
				pass
			except UnboundLocalError:
				pass
Code Block
languagepy
titleExample Job: Word Count
def create_word_count(inputfile, ext, host, fileid):
	global logger

	(fd, inputfile)=tempfile.mkstemp(suffix='.' + ext)
	try:
		# make syscall to wc
		subprocess.check_output(['wc', inputfile], stderr=subprocess.STDOUT)

		if(os.path.getsize(wcfile) == 0):
			raise Exception("File is empty.")

		# upload word count file

Calling R Scripts from Python

Coming soon...

...

Versus extractors serve to extract a signature from a file's content.  These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure.  Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure.  The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

...

Code Block
languagejava
titleAdapter
 
Code Block
languagejava
titleExtractor
 
Code Block
languagejava
titleMeasure
public class WordCountMeasure implements Serializable, Measure
{
	private static final long SLEEP = 10000;

	@Override
	public Similarity compare(Descriptor feature1, Descriptor feature2) throws Exception {
		Thread.sleep(SLEEP);
		return new SimilarityNumber(0);
	}

	@Override
	public SimilarityPercentage normalize(Similarity similarity) {
		return null;
	}

	@Override
	public String getFeatureType() {
		return WordCountMeasure.class.getName();
	}

	@Override
	public String getName() {
		return "Word Count Measure";
	}

	@Override
	public Class<WordCountMeasure> getType() {
		return WordCountMeasure.class;
	+= b1 * b1;
                } else {
                    sum += (b1 - b2) * (b1 - b2);
                }
            }

            return new SimilarityNumber(Math.sqrt(sum), 0, 1, 0);
        } else {
            throw new UnsupportedTypeException();
        }
    }
}