HI, Noted. Will look in to these and get back to you. Thanks,
On 25 February 2016 at 12:10, Maheshakya Wijewardena <mahesha...@wso2.com> wrote: > Hi Randika, > > Thank you for showing interest for this project. > > I've checked the SPMF library and what this library supports is sequential > pattern mining which is quite different from machine learning algorithms > used in WSO2 ML. What this project intends to achieve is to leverage the > existing algorithms to support streaming data. As an initiative, first you > can get an idea about the architecture of WSO2 ML[1]. CEP event streams[2] > / publishers[3] maybe used for feeding data streams in to ML. Since ML is > using Apache Spark mllib[4] for its' algorithms, you might want to read > about that. > > To get an idea about an architecture, try to understand how Spark > streaming[5] (see examples) handles input data streams. Also, have a look > in the streaming algorithms[6][7] supported. In order to use these > algorithms, you may have to use Scala APIs(Since Spark does not have Java > implementations yet). There are two approaches indicated in the project > proposals page. These streaming algorithms can be directly used in the > first approach. For the other approach, the architecture should contain a > procedure to create mini batches from streaming data with relevant sizes > (i.e. a moving window) and do periodic retraining of the same algorithm. > > BTW, watching the video referenced in the proposal (reference: 5) will > help you getting a better idea about machine learning algorithms with > streaming data. > > Let us know if you need any help with these. > > Best regards > > [1] https://docs.wso2.com/display/ML110/Architecture > [2] https://docs.wso2.com/display/CEP400/Understanding+Event+Streams > [3] https://docs.wso2.com/display/CEP400/HTTP+Event+Publisher > [4] https://spark.apache.org/docs/1.4.1/mllib-guide.html > [5] https://spark.apache.org/docs/1.4.1/streaming-programming-guide.html > [6] > https://spark.apache.org/docs/1.4.1/mllib-linear-methods.html#streaming-linear-regression > [7] > https://spark.apache.org/docs/1.4.1/mllib-clustering.html#streaming-k-means > > On Thu, Feb 25, 2016 at 10:40 AM, Randika Navagamuwa < > randika...@cse.mrt.ac.lk> wrote: > >> Hi, >> I'm a 3rd year undergraduate from Department of Computer Science and >> Engineering, University of Moratuwa. I went through the project proposals >> and I want to clarify some things regarding this project. >> >> - I've seen two approaches are mentioned, but other than those two >> methods can the objectives be achieved using this approach >> - SPMF[1] library can be used for pattern analysis. >> - Then if a data set has a same pattern as a previously modeled >> data set same algorithm can be used. >> >> According to the deliverables, first step is to come with an >> architecture. Is there any online material to refer before starting this >> project. >> >> [1]http://www.philippe-fournier-viger.com/spmf/ >> >> >> *Best Regards* >> >> *Randika Navagamuwa,* >> >> *Department of Computer Science & Engineering,* >> >> *University of Moratuwa,* >> *Sri Lanka.* >> >> *www.rnavagamuwa.com <http://www.rnavagamuwa.com>*[image: >> lk.linkedin.com/in/rnavagamuwa/] <http://lk.linkedin.com/in/rnavagamuwa/> >> [image: >> https://www.facebook.com/rnavagamuwa] >> <https://www.facebook.com/rnavagamuwa> [image: >> https://twitter.com/rnavagamuwa] <https://twitter.com/rnavagamuwa> [image: >> https://plus.google.com/+RandikaNavagamuwa/] >> <https://plus.google.com/+RandikaNavagamuwa/> >> > > > > -- > Pruthuvi Maheshakya Wijewardena > mahesha...@wso2.com > +94711228855 > > > -- *Best Regards* *Randika Navagamuwa,* *Department of Computer Science & Engineering,* *University of Moratuwa,* *Sri Lanka.* *www.rnavagamuwa.com <http://www.rnavagamuwa.com>*[image: lk.linkedin.com/in/rnavagamuwa/] <http://lk.linkedin.com/in/rnavagamuwa/> [image: https://www.facebook.com/rnavagamuwa] <https://www.facebook.com/rnavagamuwa> [image: https://twitter.com/rnavagamuwa] <https://twitter.com/rnavagamuwa> [image: https://plus.google.com/+RandikaNavagamuwa/] <https://plus.google.com/+RandikaNavagamuwa/>
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