Aggregation computation over distributed data streams(the final version)
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Aggregation computation over distributed data streams(the final version)

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I have fixed a few mistakes in the original version and appended some new contents. Likewise, I hope it is of help for you.

I have fixed a few mistakes in the original version and appended some new contents. Likewise, I hope it is of help for you.

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Aggregation computation over distributed data streams(the final version) Aggregation computation over distributed data streams(the final version) Presentation Transcript

  • A ggregation C omputation Over D istributed D ata S treams (partial content) Yueshen Xu [email_address] Middleware, CCNT Zhejiang Univ Middleware, CCNT, ZJU 12/19/11
  • Paper reference
    • What's Different: Distributed, Continuous Monitoring of Duplicate- Resilient Aggregates on Data Streams
      • Published in ICDE, 2006
      • Cited by 61 times
      • By Graham Cormode, S. Muthukrishnan etc.
    12/19/11 Middleware, CCNT, ZJU
    • I think it’s a good reading suitable for freshmen on distributed data streams
    Bell Lab Expert/27 Rutgers Expert/45 !
  • Background
    • Distributed Data Streams
      • Where and why?
        • Large scale monitoring applications
        • Many sensors distributed over a wide area
    12/19/11 Middleware, CCNT, ZJU Just one example
    • Distributed Streaming Model
    • What do we research?  Query paradigm
      • Centralized
      • Decentralized
    VS
  • Constraints and Features
    • Constraints
      • Space
        • Embedded equipments don’t have enough memory
      • Processing power
        • The same reason
      • Communication capability
        • Unreliable, spotty and sporadic
    12/19/11 Middleware, CCNT, ZJU All resources are restricted
    • Features
      • Different from ad hoc queries in DBMS, but continuous
      • Continuous is one of core characteristics in queries over data streams
    What’s different?
  • Trouble
    • Duplication  Why?
      • Wide scale monitoring invariably encounters the same events at different points
    12/19/11 Middleware, CCNT, ZJU
      • Instances
        • The same flow will be observed in different routers
        • The same individual will be observed by several mobile sensors
      • Requirement
        • Duplicate-resilient aggregate
      • Two vital questions
        • What is the amount of duplication in the network?
        • What are the versions of classical aggregates in the presence of duplicates?
      root of all evil
  • Topic
    • What kind of topics are researchers interested in ?
      • Aggregation computation
      • Routing algorithms
      • What is the aggregation?
      • Summarization, namely a statistical variable computed from the original data sets
      • Examples
        • min, max, quantile, heavy hitter
        • distinct counts, average, sum
    12/19/11 Middleware, CCNT, ZJU Not strange contacting with data streams Why only aggregation?  transaction Mirror the topic in data base
  • Problems and Concerns
    • Distinct count
    • To obtain the number of distinct data (item, record, etc) in multi-sets, namely the cardinality
    • Distinct sample
    • Important, but I’m sorry that I haven’t finished this part 
    12/19/11 Middleware, CCNT, ZJU
    • What does this paper concern about?
      • Priority: correctness, communication cost
      • Computational cost, space cost
    ! Features attached to those algorithms applied to distributed environments
    • What do we concern about dealing with aggregation computation?
      • Space complexity may be more attention-getting than time complexity
  • Distinct Counting: Flajolet-Martin Sketch
    • Flajolet-Martin Sketch
      • P. Flajolet, G. Martin . Probabilistic Counting Algorithms for Data Base Applications . Journal of Computer and System Sciences, 1985(Cited by 628)
      • Goal: To estimate the cardinalities of multi-sets of data using relative small space by one pass scan
      • The sketch is a kind of data structure, which is the way to obtain the aggregation results.  aggregation
      • I think this method can be regarded as the classical application of probability without complexity.
    12/19/11 Middleware, CCNT, ZJU
    • Give a question: How about you dealing with this problem?
      • The computing paradigm of sketching
      • Space complexity
    Be appropriate for using in data streams inherently Just think about one scene in TaoBao
  • Flajolet-Martin Sketch(Cont.)
    • Preliminary  what do we need?
      • the Multi-set M, containing all items/records, and |M| = n
      • the upper bound on the number of distinct items/records U, which is more than n
      • one bitmap, consisting of L elements, and 2 L = U
      • the hash function h(x: item/record), transforming each items into a binary string distributed uniformly over the range of [1…2 L ], just like b 1 b 2 …b L , in which b 1 is the lowest digit, and b L is the highest
      • the p(x), attaining the left most position of ‘1’
    12/19/11 Middleware, CCNT, ZJU counting not computing 1 1 … 0 0 PPT VS Whiteboard ? x record h(x) 1 L
  • Flajolet-Martin Sketch(Cont.)
    • The algorithm itself
      • the core task: remarking the position of which the leftmost ‘1’ of the hash value recorded by p(x) in bitmap B
    12/19/11 Middleware, CCNT, ZJU for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end
      • Why?
    1 0 … 1 0 Bitmap 1 L
  • Flajolet-Martin Sketch(Cont.)
    • The explanation
      • The fact: bitmap[k] equals to 1 iff after execution a pattern of the form 0 k-1 1 has appeared amongst hashed values of records in M
      • The probability: the occurrence probability of the pattern 0 k-1 1 is 1/2 k
      • Occurrence times: so if |M| = n, then bitmap[1] is accessed approximately n/2 times, bitmap[2] approximately n/4 times
      • Extension: bitmap[k] will almost certainly be zero if k >> log 2 (n) and one if k << log 2 (n) wit a fringe of 0 and 1 for k ≈ log 2 (n)
      • Selection: the leftmost 0, the rightmost 1 or something else
    12/19/11 Middleware, CCNT, ZJU U
      • The most practical part is over, and the left is very complicated taken for proving and error analysis, namely all about mathematic
    for i:=1 to L do bitmap[i] :=0 for all x in M do begin index := p(hash(x)); if bitmap[index] = the bitmap[index] :=1; end
  • Flajolet-Martin Sketch(Cont.)
    • Conclusion
      • Analysis
        • Bit-based, reducing the space complexity by constant level
        • Space complexity O(log(n))  O(log(log(n)))
        • Duplicate-insensitive  duplicate-resilient and flexible
        • Order-insensitive  stable and robust
        • Additivity  The ability to merge two FM sketches together, and the merger is simply the bitwise-or of each pair of corresponding bitmaps
      • Questions
        • How to make the value of U? What’s the relationship of U and n?
        • How to make the analysis to the error?  log(αn)
    12/19/11 Middleware, CCNT, ZJU nice qualities for distributed aggregation
  • Question
    • Is the CFV in my last report a kind of sketch?
      • Yes, I think so
    • What’s the relationship between sketch and skyline?
      • Are they the same? No, just trust me
      • I hold the opposite opinion
    • Does the aggregation computation belong to the research fields of data mining ?
      • No, I suppose it doesn’t and I don’t care
    12/19/11 Middleware, CCNT, ZJU What’s skyline?
    • Q&A
    12/19/11 Middleware, CCNT, ZJU