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Patent US8112496 - Efficient algorithm for finding candidate objects for remote differential ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsThe present invention finds candidate objects for remote differential compression. Objects are updated between two or more computing devices using remote differential compression (RDC) techniques such that required data transfers are minimized. An algorithm provides enhanced efficiencies for allowing...http://www.google.com/patents/US8112496?utm_source=gb-gplus-sharePatent US8112496 - Efficient algorithm for finding candidate objects for remote differential compressionAdvanced Patent SearchPublication numberUS8112496 B2Publication typeGrantApplication numberUS 12/533,437Publication dateFeb 7, 2012Filing dateJul 31, 2009Priority dateSep 24, 2004Also published asCN1753368A, CN1753368B, EP1641219A2, EP1641219A3, US7613787, US20060085561, US20100064141Publication number12533437, 533437, US 8112496 B2, US 8112496B2, US-B2-8112496, US8112496 B2, US8112496B2InventorsMark S. Manasse, Dan Teodosiu, Akhil WableOriginal AssigneeMicrosoft CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (185), Non-Patent Citations (144), Referenced by (1), Classifications (7) External Links: USPTO, USPTO Assignment, EspacenetEfficient algorithm for finding candidate objects for remote differential compressionUS 8112496 B2Abstract The present invention finds candidate objects for remote differential compression. Objects are updated between two or more computing devices using remote differential compression (RDC) techniques such that required data transfers are minimized. An algorithm provides enhanced efficiencies for allowing the receiver to locate a set of objects that are similar to the object that needs to be transferred from the sender. Once this set of similar objects has been found, the receiver may reuse any chunks from these objects during the RDC algorithm.
RELATED APPLICATIONS This application is a continuation of prior U.S. application Ser. No. 10/948,980, filed on Sep. 24, 2004, now U.S. Pat. No. 7,613,787 which is hereby incorporated by reference.
hash=hash^((table[b 0]<<1)|(table[b 0 ]>>u))^table[b k];hash=(hash<<1)|(hash>>63);where 1=k% 64 and u=64−1
Similarity ⁢ ( O B , O A ) ≅  { Sig B | Sig B ∈ Signatures ⁡ ( O B ) ⋀ ∃ Sig A ∈ Signatures ⁡ ( O A ) � Sig B = Sig A }   { Sig B | Sig B ∈ Signatures ⁡ ( O B ) } ⋃ { Sig A | Sig A ∈ Signatures ⁡ ( O A ) }  Given an object OB and the set of objects ObjectsA that are stored on Device A, the members of ObjectsA that have a degree of similarity with OB which exceeds a given threshold s are identified. A typical value for s may be s=0.5, (50% similarity) i.e. we are interested in objects that have at least half of their chunks in common with OB. The value for s, however, may be set at any value that makes sense for the application. For example, s could be set between 0.01 and 1.0 (1% similar to 100% similar). This set of objects is defined as:
Similar(O B,ObjectsA ,s)={O A |O A∈ObjectsA^Similarity(O B ,O A)≧s} The set of objects OA1, OA2, OAn is computed as a subset of Similar(OB, ObjectsA, s) by taking the best n matches.
IS 1 ={H 1(Shingle1), H 1(Shingle2), . . . , H 1(Shinglen)}. . . IS t ={H t(Shingle1), H t(Shingle2), . . . , H t(Shinglen} 4. At block 1340, the pre-traits PT1 . . . PTt are computed by taking the minimum element of each image set:
PT1=min(IS 1). . .PTt=min(IS t). Other deterministic mathematical functions may also be used to compute the pre-traits. For example, the pre-traits PT1 . . . PTt are computed by taking the maximum element of each image set:
PT1=max(IS 1). . .PTt=max(IS t). Mathematically, any mapping carrying values into a well-ordered set will suffice, max and min on bounded integers being two simple realizations. 5. At block 1350, the traits T1 . . . Tt are computed by selecting b bits out of each pre-trait PT1 . . . PTt. To preserve independence of the samples, it is better to choose non-overlapping slices of bits, 0 . . . b−1 for the first, b . . . 2b−1 for the second, etc, if the pre-traits are sufficiently long:
T 1=select0 . . . b−1(PT1).. . . T t=select(t−1)b . . . tb−1(PTt) Any deterministic function may be used to create traits that are smaller in size than the pre-traits. For instance, a hash function could be applied to each of the pre-traits so long as the size of the result is smaller than the pre-trait; if the total number of bits needed (tb) exceeds the size of a pre-trait, some hash functions should be used to expand the number of bits before selecting subsets. The number of traits t and the trait size b are chosen so that only a small total number of bits (t*b) is needed to represent the traits for an object. This is advantageous if the traits are precomputed and cached by Device A, as will be described below. According to one embodiment, some typical combinations of (b,t) parameters that have been found to work well are e.g. (4,24) and (6,16), for a total of 96 bits per object. Any other combinations may also be used. For purposes of explanation, the ith trait of object A will be denoted by Ti(A).
Traitsimilarity(O B ,O A)=|{i|T i(A)=T i(B)}|SimilarTraits(O B,ObjectsA ,x)={O A |O A∈ObjectsA{circumflex over (0)}TraitSimilarity(O B ,O A)≧t} Other computations from which these values might be derived would work just as well.
MinP={P k |Λj∉[1,t]�TS j └P j ┘≧TS k └P k┘} Let MinID be the minimum object ID pointed to by all the indices in MinP. 4. At block 1840, Let k=|MinP|, which corresponds to the number of matching traits. If k>x and if ObjectMap(MinP) is not a dead entry, then append MinID to OBk. 5. Advance every index Pk in MinP to the next object ID in its respective TraitSet array TSk. Go to step 2 (block 1820). 6. At block 1860, select the similar objects by first selecting objects from OBt, then from OBt−1, etc., until the desired number of similar objects has been selected or no more objects are left in OBx. The object IDs produced by the above steps can be easily mapped into object references by using the ObjectMap. The above specification, examples and data provide a complete description of the manufacture and use of the composition of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims hereinafter appended.
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