Hi Mohan, As I said please ignore the cep-877.patch and use the improved.cep-877. I cloned a new repo and ran following commands to apply the patches and worked fine.
149-160-172-65:siddhi lahirugunathilake$ git apply --check cep-873.patch 149-160-172-65:siddhi lahirugunathilake$ git am --signoff < cep-873.patch Applying: fixing issue https://wso2.org/jira/browse/CEP-872 149-160-172-65:siddhi lahirugunathilake$ git apply --check improved.cep-877 149-160-172-65:siddhi lahirugunathilake$ git am --signoff <improved.cep-877 Applying: adding Lossy Counting algorithm as a window implementation Applying: improving the counting algorithm to count based on each attribute and adding test-cases for both algorithms Applying: modifying lossy outputs not to emit tuples even they already exists but always have to match the frequency range Applying: reverting the pom file changes 149-160-172-65:siddhi lahirugunathilake$ Regards Lahiru On Jun 30, 2014, at 7:31 AM, Mohanadarshan Vivekanandalingam wrote: > Hi Lahiru, > > I have gone through above patches and play around with it little bit.. > CEP873.patch seems fine and i have done some basic testing But i have got > some issues when applying CEP877.patch. Even-though i am able to get rid off > that after making some changes, I think it is better if you send proper patch > for that. > > Will commit these improvements once i got the patch for CEP877. > > Thanks, > Mohan > > > > On Mon, Jun 30, 2014 at 10:27 AM, Mohanadarshan Vivekanandalingam > <[email protected]> wrote: > > > On Sat, Jun 28, 2014 at 2:22 PM, Lahiru Gunathilake <[email protected]> > wrote: > Hi Mohan, > > I have attached the latest patch for cep-877(I removed the old patch from the > jira). But the very first patch I have attached in CEP-873 is required by > this patch. > > I made few improvements to both the algorithms where you can give the > attributes you want to count. Initial version I did was to count distinct > tuples but practically I think counting distinct attributes is going to be > useful. If user doesn't give any attribute I simply count the distinct tuple > with all the attributes. > > I have added two test cases but I can see in the build cluster test cases are > removed, I did test locally only with my test cases and worked fine. > > If you have any issues with the two patches please let me know. > > > OK Lahiru, I'll go through that Today... > > Thanks, > Mohan > > Thanks > Lahiru > On Jun 27, 2014, at 2:03 AM, Seshika Fernando wrote: > >> Hi Lahiru, >> >> As Srinath has mentioned as well, frequency counting algorithms are very >> useful in financial market scenarios as well (especially fraud detection and >> surveillance). >> Thanks for doing this and I will take a look too. >> >> seshika >> >> >> On Fri, Jun 27, 2014 at 10:52 AM, Mohanadarshan Vivekanandalingam >> <[email protected]> wrote: >> >> >> >> On Fri, Jun 27, 2014 at 10:00 AM, Lahiru Gunathilake <[email protected]> >> wrote: >> Hi Mohan, >> >> Hi Lahiru, >> >> >> I wrote some samples but I can write more test-cases and provide another >> patch. Please feel free to change the naming of the windows as you like. >> >> Really appreciate your contribution.. Sure, I'll start look into this.. >> >> Thanks, >> Mohan >> >> >> Regards >> Lahiru >> >> On Jun 26, 2014, at 11:35 PM, Srinath Perera wrote: >> >>> Hi All, >>> >>> Lahiru and myself had a call today morning. >>> >>> Plan is to >>> 1) Lahiru to look at hoeffding tree and other classification algorithms and >>> select one to implement. He will compare the performance against MOA or >>> some other implementation. >>> 2) then we will use it for a Fraud analysis scenario as a proof of its >>> validity. >>> >>> Then we will decide how to continue from that point. >>> >>> Mohan could you look at the patch? Lahiru will write test cases that you >>> can use to verify. >>> >>> --Srinath >>> >>> >>> On Tue, Jun 24, 2014 at 4:56 AM, Lahiru Gunathilake <[email protected]> >>> wrote: >>> Hi Srinath, >>> >>> Thanks for the response. >>> On Jun 23, 2014, at 10:48 PM, Srinath Perera wrote: >>> >>>> Hi Lahiru, >>>> >>>> Sorry for not responding earlier. I was traveling last week. >>>> >>>> I guess you know frequent item set != clustering algorithms. >>> +1 >>> >>>> >>>> Can we have a chat sometime to discuss details about the implementation. >>> Yes sure, that would be very useful. >>> >>> Thanks >>> Lahiru >>> >>>> >>>> --Srinath >>>> >>>> >>>> On Sun, Jun 22, 2014 at 6:25 PM, Lahiru Gunathilake <[email protected]> >>>> wrote: >>>> Hi All, >>>> >>>> I have implemented another frequency counting algorithm[1] which is a >>>> classic algorithm for mining frequent items in a data stream. Basically >>>> users can specify a minimum average value of frequent items with an error >>>> value. >>>> >>>> This algorithm will accept two user-specified parameters: a support >>>> threshold s [0-1] and an error parameter e [0-1] such that e << s and >>>> recommended number for e is normally s/10 or s/20. Let N denote the >>>> current length >>>> of the stream, i.e., the number of tuples seen so far. At any point of >>>> time, this algorithm can be asked to produce a list of events along with >>>> their estimated frequencies. The answers produced by this algorithm will >>>> have the >>>> following guarantees: >>>> 1. All item(set)s whose true frequency exceeds sN are outputs. >>>> There are no false negatives . >>>> 2. No events whose true frequency is less than (s - e) N >>>> is output. >>>> 3. Estimated frequencies are less than the true frequencies >>>> by at most eN. >>>> . >>>> The incoming stream is conceptually divided in to buckets of width w = >>>> ceiling(1/e) transactions each. Buckets are labeled with bucket ids , >>>> starting from 1.We denote the current bucket id by bcurrent whose value >>>> is ceiling(N/w). For an element , we denote its true frequency in the >>>> stream seen so far by "fe" . Note that e and w are fixed for a data >>>> stream while N, bcurrent and fe are the variables whose value changes when >>>> the stream progress. >>>> Here the data structure , is a set of entries are tuples with the form of >>>> (event, f, delta), where event is the actual event and "f" is the is an >>>> integer representing its estimated frequency, and delta is the maximum >>>> possible error in "fe". >>>> >>>> In this algorithm every new event will anyways added in to the >>>> data-structure optimistically and there is no initial condition to enter >>>> in to the data-structure but iteratively if certain events are not match >>>> to the condition provided >>>> by the user those events will be removed when N%windowSize = 0, during >>>> this I output those events as expired-events in the window. For the >>>> incoming events if event is already exists I output those as >>>> current-events and if its a new >>>> event I just add it to the data-structure and iterate through all the >>>> events available and find the matching events based on s and e values and >>>> only those events will be output. >>>> >>>> I am not sure based on the window definition this is the correct approach >>>> or may be windows aren't the best way to associate this algorithm in to >>>> siddhi. I have attached my patch to jira[2] and if you can look that would >>>> be great. >>>> >>>> Siddhi Query will looks like below, >>>> from cseEventStream#window.lossyFrequent(0.1,0.01) " + >>>> "select symbol, >>>> price " + >>>> "insert into >>>> StockQuote; >>>> >>>> >>>> [1]Gurmeet Singh Manku and Rajeev Motwani. 2002. Approximate frequency >>>> counts over data streams. In Proceedings of the 28th international >>>> conference on Very Large Data Bases (VLDB '02). VLDB Endowment 346-357. >>>> >>>> Regards >>>> Lahiru >>>> On Jun 19, 2014, at 2:36 AM, Lahiru Gunathilake wrote: >>>> >>>>> Hi All, >>>>> On Jun 17, 2014, at 1:49 AM, Lahiru Gunathilake wrote: >>>>> >>>>>> Hi All, >>>>>> >>>>>> I am planning to evaluate different event stream clustering algorithms >>>>>> as part of my studies(I am a graduate student at indiana University). I >>>>>> think Siddhi is a good place to experiment this, As per my understanding >>>>>> based on the docs Siddhi doesn't have a stream clustering interface I >>>>>> can use directly to plug my own algorithm. So I am thinking of first >>>>>> come up an interface for different clustering algorithms and add >>>>>> implementation of algorithms for each event stream by invoking an >>>>>> operation like SiddhiManager.addQuery. Or I can make the algorithm >>>>>> configure as part of query language. If the second option is more >>>>>> consistent with current model I can wrap-up the work in that way but >>>>>> initially focussing on first approach will be easier for me. So each >>>>>> algorithm can be associated to a desired event Stream or can be >>>>>> associated globally. If its associated with each stream algorithm will >>>>>> run local to each stream otherwise it will run in global context. Based >>>>>> on the algorithm I can provide a way to configure it with parameters. >>>>>> >>>>> I am sure I have confused with above implementation details, after >>>>> looking in to Siddhi extension points I figured out I just have to >>>>> implement a new window type. I have implemented one algorithm to keep the >>>>> most frequent events >>>>> came in a event stream. So queries can looks like below, >>>>> >>>>> from cseEventStream#window.frequent(2) " + >>>>> "select symbol, >>>>> price " + >>>>> "insert into >>>>> StockQuote; >>>>> >>>>> There are multiple algorithms to keep the most frequent events in a given >>>>> window size for now I just implemented a simple algorithm[1] with the >>>>> processing complexity of O(1) and space complexity O(n) where n is the >>>>> limit of the most frequent items. I have created a patch and attached it >>>>> to jira[2]. >>>>> >>>>> [1] Jayadev, and David Gries Misra, "Finding >>>>> repeated elements," in Science of computer >>>>> programming 2, no. >>>>> 2 (1982): 143-152. >>>>> [2]https://wso2.org/jira/browse/CEP-877 >>>>> >>>>> Thanks >>>>> Lahiru >>>>>> To start this I hope to implement a frequent item set mining algorithm >>>>>> which can be used to find out most frequent items of an event stream. >>>>>> Search engines use these kind of data to find out most frequent searches >>>>>> in a given time window and optimize the search queries. I can start with >>>>>> some algorithms like Misra-Gries algorithm[1] and Manku and >>>>>> Motwani [2] and then move towards more of data clustering algorithms. >>>>>> For the time being I will write the clustering results in to a file and >>>>>> later I think I can use more stable storage (either wso2 registry or >>>>>> other prefered way in wso2 product stack). If Siddhi or WSO2 CEP already >>>>>> have the capability of frequent item mining I will start with a more >>>>>> classification type algorithm. >>>>>> >>>>>> Your feedback will be very useful for my work. If you have requirement >>>>>> for any specific type of algorithms based on the real client >>>>>> interactions you have, I would like to know them and implement them with >>>>>> Siddhi and do the comparison. >>>>>> >>>>>> Thanks >>>>>> Lahiru >>>>>> _______________________________________________ >>>>>> Architecture mailing list >>>>>> [email protected] >>>>>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>>>> >>>>> _______________________________________________ >>>>> Architecture mailing list >>>>> [email protected] >>>>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>>> >>>> >>>> _______________________________________________ >>>> Architecture mailing list >>>> [email protected] >>>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>>> >>>> >>>> >>>> >>>> -- >>>> ============================ >>>> Srinath Perera, Ph.D. >>>> http://people.apache.org/~hemapani/ >>>> http://srinathsview.blogspot.com/ >>>> _______________________________________________ >>>> Architecture mailing list >>>> [email protected] >>>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>> >>> >>> _______________________________________________ >>> Architecture mailing list >>> [email protected] >>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >>> >>> >>> >>> >>> -- >>> ============================ >>> Srinath Perera, Ph.D. >>> http://people.apache.org/~hemapani/ >>> http://srinathsview.blogspot.com/ >>> _______________________________________________ >>> Architecture mailing list >>> [email protected] >>> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >> >> >> >> >> -- >> V. Mohanadarshan >> Software Engineer, >> Data Technologies Team, >> WSO2, Inc. http://wso2.com >> lean.enterprise.middleware. >> >> email: [email protected] >> phone:(+94) 771117673 >> >> _______________________________________________ >> Architecture mailing list >> [email protected] >> https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture >> >> > > > _______________________________________________ > Architecture mailing list > [email protected] > https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture > > > > > -- > V. Mohanadarshan > Software Engineer, > Data Technologies Team, > WSO2, Inc. http://wso2.com > lean.enterprise.middleware. > > email: [email protected] > phone:(+94) 771117673 > > > > -- > V. Mohanadarshan > Software Engineer, > Data Technologies Team, > WSO2, Inc. http://wso2.com > lean.enterprise.middleware. > > email: [email protected] > phone:(+94) 771117673 > _______________________________________________ > Architecture mailing list > [email protected] > https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture
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