Real-Time Data Analysis in ClowdFlows

Janez Kranjc1,2

Vid Podpecan11Jozef Stefan Institute

2International Postgraduate School Jozef StefanJamova 39, 1000 Ljubljana, Slovenia

janez.kranjc@ijs.si

Nada Lavrac1,33University of Nova Gorica

Vipavska 13, 5000 Nova Gorica, Slovenia

Abstract—ClowdFlows is an open cloud based platformfor composition, execution, and sharing of interactive datamining workflows. In this paper we extend the ClowdFlowsplatform with the ability to mine real-time data streams. Thisfunctionality was implemented by creating a specialized typeof workflow component and a stream mining daemon thatdelegates the execution of workflows in real-time. In this way,we have transformed a batch data processing platform intoa real-time stream mining platform with an intuitive userinterface. The real-time analytics aspect of the platform isdemonstrated in a Twitter sentiment analysis use case wherethe sentiment of tweets about whistleblower Edward Snowdenwas monitored for approximately one month.

Keywords-data mining platform; stream mining; real-timedata analysis; web application; workflows; sentiment analysis

I. INTRODUCTION

During recent years performing data mining tasks has be-come synonymous with constructing data mining workflowsin specialized knowledge discovery and data mining envi-ronments. In contrast to traditional data analysis softwarewhich supported a single or few algorithms designed tomine specialized data, current knowledge discovery systemsprovide large collections of generic algorithm implementa-tions coupled with an easy-to-use graphical user interface.The common feature of all these data mining tools is theconcept of visual programming: all advanced knowledgediscovery systems offer some form of workflow constructionand execution engine. This allows practitioners and non-experts to perform data mining tasks by using simple dragand drop operations on workflow components and connectthem to form workflows. Performing real-time data analysison complex and geographically dispersed information andknowledge sources can also be simplified by providingpractitioners with simple ways to construct real-time datamining workflows using a specialized data mining platform.

ClowdFlows1 is a new data mining platform designedto overcome several deficiencies of similar data miningplatforms and to provide novel features to benefit the datamining community by allowing mining of big data in real-time by connecting to different data streams. This paper

1http://clowdflows.org/

presents the process of transforming the ClowdFlows plat-form to a real-time data mining platform. The platformwas first developed as a traditional data mining tool forprocessing static data, which successfully bridges differentoperating systems and platforms, and is able to fully utilizeavailable server resources in order to relieve the client fromheavy-duty processing and data transfer as the platform isentirely Web based and can be accessed from any modernbrowser [1]. ClowdFlows also benefits from a service-oriented architecture which allows users to utilize arbitraryWeb services as workflow components.

The paper is structured as follows. Section II presentscomparable data mining and stream mining platforms anddescribes their differences and similarities with ClowdFlows.The technical background and implementation of the plat-form is presented in Section III. Section IV presents theprocess of adapting ClowdFlows to work on real-time datastreams. In Section V we present a use case for mining theTwitter sentiment on an arbitrary query in real-time. Finally,in Section VI we conclude the paper with ideas for furtherwork.

II. RELATED WORK

This section describes similar data mining platforms,some of which also support stream mining. The platformsare grouped together based on the features they offer.The important features of these platforms are: the imple-mentation of the visual programming paradigm, service-oriented approach to knowledge discovery, remote workflowexecution, workflow sharing, and functionalities that allowreal-time data analysis. The differences and similaritiesbetween ClowdFlows and these platforms are also noted andexplained. The related work is separated in two categories,based on whether it focuses on batch or stream processing.

A. Data mining tools and batch processing

The majority of data mining software solutions fromdifferent domains allow construction and execution of scien-tific workflows. Known examples of such platforms includeWeka [2], Orange [3], KNIME [4], and RapidMiner [5] data

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mining environments. These platforms all implement a work-flow canvas which is a part of the graphical user interfacewhere workflows can be constructed by using drag, dropand connect operations on the available components. Theworkflow components (parts of the graphical user interface)are often reffered to as widgets. The range of available com-ponents typically includes data loading and pre-processing,data and pattern mining algorithms and interactive and non-interactive visualizations. The graphical user interfaces ofthese platforms are developed in Java (with the exception ofOrange which is written in Python), and need to be installedon the user’s computer. Since ClowdFlows is a Web basedplatform, the graphical user interface is written entirely inHTML and JavaScript, which allows it to be accessed froma Web browser.

In order to benefit from service-oriented architectureconcepts, software tools have emerged, which are able tomake use of Web services, and can access large publicdatabases (some supporting means of grid deployment andP2P computing). Environments such as Weka4WS [6], Or-ange4WS [7], Web Extension for RapidMiner, Triana [8],Taverna [9] and Kepler [10] allow for the integration of Webservices as workflow components. However, with the ex-ception of Orange4WS and Web Extension for RapidMiner,these environments are mostly specialized in domains likesystems biology, chemistry, medical imaging, ecology andgeology.

Remote workflow execution (on different machines thanthe one used for workflow construction) is employed byKNIME and RapidMiner using the RapidAnalytics server.This allows the execution of workflows on more powerfulmachines and data sharing with other users, with the re-quirement that the client software is installed on the user’smachine. The client software is still used for designingworkflows which are executed on remote machines, whileonly the results can be viewed using a Web interface.

Sharing data and experiments has been implemented inthe Experiment Database [11], which is a database of stan-dardized machine learning experimentation results. Insteadof a workflow engine it features a visual query engine forquerying the database, and an API for submitting experi-ments. Sharing of workflows has also been implemented atthe myExperiment website [12]. Users can save workflowsfrom several data mining platforms and upload them to themyExperiment website. Using the myExperiment website asa medium for sharing workflows still requires installation ofan appropriate data mining platform on the user’s computer.ClowdFlows supports sharing data and workflows nativelyby providing users with special URLs for their workflowsthat can be accessed from anywhere and by anyone (ifpermitted by the author of the workflow).

B. Data mining tools and stream processing

Stream classification and clustering was implemented inthe MOA framework [13]. MOA is related to Weka andalso written in Java. The MOA framework however cannotprocess streams in ad distributed environment.

Distributed Stream Processing Engines have been thefocus of much research and development recently. One of thefirst open source Stream Processing Engines available wasBorealis [14]. The SAMOA project2 is an upcoming projectthat will provide the MOA functionalities using the S4 [15]and Storm3 SPEs to create a distributed stream mining plat-form. ClowdFlows implements its own distributed StreamProcessing Engine which is based on its own workflowexecution engine used for batch processing. The engine issimpler in comparison to Storm or S4, but easy to install andrequires no programming skills to use, due to its graphicaluser interface.

III. THE CLOWDFLOWS PLATFORM

The development of the ClowdFlows platform is presentedin this section. The enabling technologies are describedbriefly and displayed in the architecture of the system. Thegraphical user interface and the basic usage of ClowdFlowsare presented along with its workflow execution engine.

A. Architecture of the platform and technologies used

The ClowdFlows platform is first and foremost a Webapplication. It consists of mainly two parts: the server-side and the client-side. The server side is the part of theapplication that is executed on a server or a cluster of servers.The client side present the parts of the system that areexecuted on the user’s computer. The architecture of thesystem being accessed by multiple users is illustrated inFigure 1.

The graphical user interface is implemented in HTMLand CSS, so that it may be rendered in Web browsers.The functionalities of the user interface were implementedin JavaScript using the jQuery library4. The graphical userinterface is accessed with a Web browser. A public instal-lation of ClowdFlows is avialable at the following URL:http://clowdflows.org.

The server side is written in Python using the Djangoframework5. Python was selected because it allowed easyintegration of many machine learning and data mining toolsimplemented in Python, such as Orange [3] and Scikit-learn [16]. The Django framework is a high-level PythonWeb framework that encourages rapid development andclean, pragmatic design, and is the most popular Web frame-work for Python. It provides an object-relational mapper anda powerful template system. The object-relational mapper

2http://samoa-project.net/3http://storm-project.net/4http://jquery.com/5https://www.djangoproject.com/

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Figure 1. The architecture of the ClowdFlows system.

provides an API that links objects to a database, whichmeans that the ClowdFlows platform is database agnostic.PostgreSQL, MySQL, SQLite and Oracle databases are allsupported, although MySQL is used in the public installationof ClowdFlows.

ClowdFlows may also be installed on multiple computers,which is enabled by using the RabbitMQ6 messaging serverand broker and a Django implementation of Celery7, adistributed task queue, which allows passing asynchronoustasks between servers. Workers that execute tasks can beinstalled on a cluster of machines, or on a single machine.

To enable consumption of Web services and using them asworkflow components, the PySimpleSoap library8 is used.PySimpleSoap provides an interface for client and serverWeb service communication, which allows importing WSDL

6http://www.rabbitmq.com/7http://celeryproject.com/8https://code.google.com/p/pysimplesoap/

Figure 2. A screenshot of the ClowdFlows graphical user interface loadedin the Google Chrome Web browser.

Web services as workflow components, and exposing entireworkflows as WSDL Web services.

B. The Graphical User Interface

The graphical user interface follows the visual program-ming paradigm for constructing workflows. This simplifiesthe representation of complex procedures into a spatialarrangement of building blocks. The building blocks orworkflow components in ClowdFlows are called widgets.Each building block receives some data or parameters at theinput, performs a specific task, and return the results on itsoutputs. Workflows consist of widgets and connections thatconnect outputs of widgets to inputs of other widgets.

The graphical user interface of the ClowdFlows platformconsists of two main parts: the widget repository and theworkflow canvas. A screenshot of the system can be seenin Figure 2. The widget repository is located on the leftside of the screen and the workflow canvas on the right.The workflow canvas is in the screenshot features a trivialworkflow for building and displaying a J48 decision tree.

The widget repository is a taxonomy of workflow com-ponents that can be put on the canvas by clicking onthem. Widgets in ClowdFlows belong into four basic groups.Regular widgets are widgets that simply take data at theinput, perform a specific task and return data on theiroutputs. Visualization widgets can render results as HTMLand Javascript and display it in dialog windows in thebrowser. Interactive widgets can open a modal window toquery the user for additional data during run-time. Finally,workflow control widgets allow creating sub-workflows (i.e.a widget that contains a workflow) and for loops. Thewidgets repository contains widgets from several differentdata mining platforms. Orange widgets were implementedinto ClowdFlows as they are both written in Python. Wekaalgorithms were exposed as Web services and imported intothe platform. The scikit-learn machine learning library inPython was also implemented as a set of widgets.

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The workflow canvas implements moving, connecting,deleting and issuing commands to execute widgets andworkflows. Widgets can be arbitrarily arranged on the canvasby dragging and dropping. Connections between widgets arecreated by selecting an output of a widget and an inputof another widget. Whenever a user performs an action onthe graphical user interface it is sent to the server using anasynchronous HTTP POST request. Operations are validatedon the server and the success or error messages are passed tothe client’s browser formatted in JavaScript Object Notation(JSON) or HTML.

A toolbar is located above the workflow canvas and canbe used to save, copy, and delete entire workflows. Whena workflow is saved it can also be made public. Publicworkflows get specialized URLs, which can be shared sothat other people can access these workflows.

C. The Workflow Engine

The workflow execution engine executes all executablewidgets in the workflow in the correct order. The engineoperates with widgets and assigns them different states basedon their state of execution.

A state of each widget can be one of the following:executable, pending, running, finished, or failed. The goalof the workflow execution engine is to have no widgets inthe executable or running state in the workflow.

Executable widgets are widgets whose predecessors’ stateis finished, or have no predecessors at all. A predecessor ofa widget is a widget whose output is connected to an inputof its descendant. Pending widgets are widgets whose stateis neither executable, running, finished nor failed. Runningwidgets are widgets that have been executed but havenot yet finished or failed. Finished and failed widgets arewidgets that completed the execution either successfully orunsuccessfully. A failed or finished widget becomes pendingor executable whenever a connection leading towards thewidget is deleted, added, or changed, or when a value ofthis widget’s parameter is changed, or when a user manuallychanges the state of the widget to executable.

When a workflow is running the execution engine perpet-ually checks for widgets that are executable and executesthem. Whenever two or more widgets are executable at thesame time they are asynchronously executed in parallel,since they are independent. The widget state mechanismensures that no two widgets where the inputs of a widget aredependent on an output of another widget will be executableat the same time. The execution of a workflow is completewhen there are no executable or running widgets.

IV. REAL-TIME DATA ANALYSIS IN CLOWDFLOWS

This section describes the adaptation of ClowdFlows toenable real-time data processing. The workflow executionengine has been augmented with continuous parallel execu-tion and the halting mechanism. Specialized widgets that are

useful in real-time processing workflows are also describedin this section.

A. Continuous workflow execution with the halting mecha-nism

Regular workflows and stream mining workflows areprimarily distinguished by their execution times. A widgetin a static workflow is executed a finite amount of timesand the workflow has a finite execution time. Widgetsin a stream mining workflow are executed a potentiallyinfinite amount of times and the workflows are executeduntil manually terminated by users. Another major differencebetween regular workflows and stream mining workflows isthe data on the input. The data that is processed by regularworkflows is available in whole during the entire processingtime, while data entering the stream mining workflows ispotentially infinite and is only exposed as a small instanceat any given time.

In order to handle potentially infinite data streams wehave modified the workflow execution engine to execute theworkflow multiple times at arbitrarily small temporal inter-vals in parallel. The amount of parallelism and the frequencyof the execution are parameters that can be (providing thehardware availability) modified for each stream to maximizethe throughput.

The execution of the workflows is delegated by a specialstream mining daemon that issues tasks to the messagingqueue. The stream mining daemon’s task is to issue com-mands to execute streaming workflows. The daemon can alsoprioritize execution of some streams over others based on theusers’ preferences. Tasks are picked up from the messagingqueue by workers that execute the workflow. To ensure thateach execution of a workflow processes a different instanceof the data, special widgets and mechanisms were devel-oped, which can halt the execution of streaming workflows.This halting mechanism can be activated by widgets in astreaming workflow to halt the current execution.

Workflows that are executed as a stream mining processneed to be saved as streaming workflows and executedseparately. The user cannot inspect the execution of theworkflow in real time, as many processes are running inparallel. The user can, however, see results from specialstream visualization widgets.

B. Specialized workflow widgets for real-time processing

Widgets in stream mining workflows have, in contrastto widgets in regular workflows, internal memory and theability to halt the execution of the current workflow. Internalmemory is used to store information about the data stream,such as the timestamp of the last processed data instance, oran instance of the data itself. These two mechanisms wereused to develop several specialized stream mining widgets.

In order to process data streams, streaming data inputshad to be implemented. Each type of stream requires its

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own widget to consume the stream. In principle, a streaminginput widget connects to an external data stream source,collects instances of the data that it has not yet seen, and usesits internal memory to remember the current data instances.This can be done by saving small hashes of the data, topreserve space or just the timestamp of the latest instanceif they are available in the stream itself. If the input widgetencounters no new data instances at the stream source ithalts the execution of the stream. No other widgets that aredirectly connected to it via its outputs will be executed untilthe workflow is executed again.

Other popular stream mining approaches [17] were alsoimplemented as workflow components. The aggregationwidget was implemented to collect a fixed number of datainstances before passing the data to the next widget. Theinternal memory of the widget is used to save the datainstances until the threshold is reached. While the numberof the instances is below the threshold, the widget haltsthe execution. The internal memory is emptied and the datainstances are passed to the next widget once the thresholdhas been reached.

The sliding window widget is similar to the aggregationwidget, except that it does not empty its entire internalmemory upon reaching the threshold. Only the oldest fewinstances are forgotten and the instances inside the slidingwindow are released to other widgets in the workflow forprocessing. By using the sliding window, each data instancecan be processed more than once.

Sampling widgets are fairly simple. They either pass theinstance to the next widget or halt the execution, based on anarbitrary condition. This condition can be dependent on thedata or not (e.g. drop every second instance). The internalmemory can be used to store counters, which are used todecide which data is left in the sample.

Special stream visualization widgets were also developedfor the purpose of examining results of real-time analyses.Each instance of a stream visualization widget creates aspecial web page with a unique URL that displays the resultsin various formats. This is useful because results can beshared without having to share the actual workflows. Anexample of such a widget is presented in section V.

V. TWITTER SENTIMENT ANALYSIS USE CASE

In this section we present a use case that showcases someof the features of the ClowdFlows real-time data processingplatform. The description of the use case is written as astep-by-step report on how the workflow was constructed.Following the description in this section, it is possible forthe reader to construct a fully functioning stream miningworkflow and view the results.

The aim of the use case is to monitor the Twitter sentimenton a given subject. For the purpose of this use case wehave selected to monitor tweets containing the keywordSnowden, as it is one of the trending keywords during the

time of writing this article. We wish to measure the Twittersentiment over time regarding Edward Snowden, who leakeddetails of several top-secret documents to the press.

A. The development of necessary components

To construct the workflow we required a stream inputthat would collect tweets based on a query, a samplingwidget that would discard any non-English tweets, a widgetto perform sentiment analysis on tweets, a stream splitter tosplit the stream of tweets in to a stream of positive and astream of negative tweets, and three types of visualizationwidgets to display the line chart of the sentiment over time,a word cloud of positive or negative tweets, and the latesttweets.

To consume the incoming stream we implemented awidget that connects to Twitter via the Twitter API9. Thewidget accepts several parameters: the search query, bywhich it filters the incoming tweets, the geographical loca-tion (optional), which filters tweets based on location, andthe credentials for the Twitter API. The widget works both ina streaming and non-streaming environment. Whenever thewidget is executed it will fetch the latest results of the searchquery. For streaming workflows, the internal memory of thewidget holds the ID of the latest tweet, which is passed tothe Twitter API, so that only the tweets that have not yetbeen seen are fetched.

Since tweets returned by the Twitter API are annotatedwith their language, we constructed a widget for filteringtweets based on their language. This widget discards alltweets that are not in English.

There are three common approaches to perform sentimentclassification [18]: machine learning based, lexicon basedand linguistic analysis. In our case we used an SVM classi-fier (machine learning based) that was trained on a labelleddataset and implemented in the .NET framework [19]. Inorder to use it in the ClowdFlows platform we exposed theclassifier as a Web service and imported it in the platform.

A simple widget was implemented that splits the stream oftweets into two streams, based on their sentiment. This wasdone so that positive and negative tweets could be separatelyinspected.

To visualize the sentiment we implemented a line chartthat displays the volume of all tweets, volume of positivetweets, negative volume of negative tweets, and the differ-ence of positive and negative tweets. The visualization wasimplemented with the HighCharts JavaScript visualizationlibrary10.

To inspect separate tweets a simple table was implementedwhere each tweet is coloured red or green based on itssentiment (red for negative and green for positive).

The word cloud visualization was implemented to showmost popular words in recent tweets. This visualization is

9https://dev.twitter.com/10http://www.highcharts.com/

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Figure 3. The Twitter sentiment analysis workflow.

dynamic and changes with the stream. By looking at theword cloud and see popular words appearing and unpopularwords disappearing is a novel way to look at data streamsin real-time. The visualization was developed with the D3.jsJavaScript library11.

B. Constructing the workflow

The workflow was constructed using the ClowdFlowsgraphical user interface. Widgets were selected from thewidget repository and added to the canvas and connectedas shown in Figure 3.

After connecting the widget into the workflow, someparameters were set. Parameters of a widget are set bydouble clicking the widget. Twitter API credentials and thesearch query were entered as parameters for the Twitterwidget. The language code en was entered as a parameterof the Filter tweets by language widget. We have also addedthree sliding window widgets with the size 500 (enteredas parameter) to the workflow. This is done because thevisualization widgets that display tweets and word cloudsonly display the last data that was received as an input forthese widgets. By setting the size of the window to 500the word cloud will always consist of the most recent 500tweets.

The workflow was saved by clicking the save button inthe toolbar. We have also marked the workflow as public sothat the workflow can be viewed and copied by other people.The URL of the workflow is http://clowdflows.org/workflow/1041/. We have then navigated to the workflows page

11http://d3js.org/

(http://clowdflows.org/your-workflows/) and clicked the but-ton ”Start stream mining” next to our saved workflow. Bydoing this we have instructed the platform to start executingthe workflow with the stream mining daemon. A specialweb page was created where detailed information about thestream mining process is displayed. This page also containslinks to visualization pages that were generated by thewidgets. The stream mining process was left running fromthe 14th of June until the 10th of July.

C. Monitoring the results

We have put several stream visualization widgets in theworkflow which allowed us to inspect the results during theprocess of stream mining. ClowdFlows has generated a Webpage for each stream visualization widget, which can beviewed by anybody since the workflow is public.

The sentiment graph visualization displaying the line chartof volumes of tweets, volumes of positive tweets, negativevolume of negative tweets and the difference of postiveand negative sentiment is available at http://clowdflows.org/streams/data/4/9056/ and is displayed in Figure 4. Bylooking at this visualization we can see that the generalsentiment in the tweets mentioning Snowden is generallymore positive than negative. We can observe several spikesin volume which correspond to the times when news articlesregarding this subject were published. On June 23rd, news ofEdward Snowden’s departure from Hong Kong and arrivalin Moscow was published. On the first of July EdwardSnowden released a statement on the Wikileaks website andlots of news reports focused on possible countries that couldoffer asylum to Edward Snowden.

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Figure 4. A line chart of sentiment, volume, and sentiment difference over time.

Figure 5. A word cloud constructed from tweets with a negative sentiment.

The word cloud visualization of negative tweets is avail-able at http://clowdflows.org/streams/data/4/9065/ and isshown in Figure 5. This visualization helps put the streaminto another perspective and can display changing trends inreal-time. When the word cloud is opened in the browserand the stream mining process is active the words changepositions and sizes corresponding to their occurrences in thetweets.

Links to the visualizations of other stream visualizationwidgets are also present on the two provided visualizationpages.

The workflow presented in this use case is general andreusable. The query chosen for monitoring was arbitrary andcan be trivially changed. This type of workflow could alsobe used for monitoring sentiment on other subjects, suchas monitoring the Twitter sentiment of political candidatesduring an election, or monitoring the sentiment of financialtweets with stock symbols as queries.

VI. CONCLUSION AND FURTHER WORK

This paper presents a data mining platform for construct-ing and executing scientific workflows, and the adaptation ofthis platform to suit the needs of real-time data processing. It

implements the visual programming paradigm, which is usedfor presenting complex procedures as a sequence of simplesteps, which renders the platform usable for non-experts.

We have demonstrated the simplicity and usability of theplatform on a Twitter sentiment analysis use case whichcollects tweets for a given query, filters the tweets based onlanguage and classifies the sentiment of the tweet as positiveor negative. The platform produces results in forms of a wordcloud and a line chart displaying the positive sentiment, thenegative sentiment, and the difference between the positiveand negative sentiment. The workflow constructed in this usecase is reusable and can be executed for any given query tomonitor sentiment on Twitter for arbitrary subjects. Whilethis use case required implementing some new workflowcomponents to work with tweets and the Twitter API, thesecomponents are now available and completely reusable inother stream mining workflows.

ClowdFlows currently boasts more than two hundredwidgets for batch processing and only fourteen for streammining. It is our aim to create more specialized widgetsfor stream mining so that they can be used by practitionerswithout the need to develop their own components.

In future work, we will also add mechanisms to ensurefault-tolerance and guarantee the processing of all the datafrom the incoming streams. We plan to release the sourcesof the platform under an open source licence, and developscripts that will support single-click deployment of Clowd-Flows to cloud services such as Amazon Elastic ComputeCloud (EC2).

ACKNOWLEDGMENT

This work was supported by the FP7 European Commis-sion projects “Machine understanding for interactive story-telling” (MUSE, grant agreement no: 296703) and “Largescale information extraction and integration infrastructurefor supporting financial decision making” (FIRST, grantagreement 257928).

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The authors would like to thank Matjaz Jursic for hishelp in developing ClowdFlows, and Jasmina Smailovic fordeveloping the SVM sentiment classifier.

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