Method and system for identifying complementary data objects
A data object and identification technology, applied in the direction of electronic digital data processing, special data processing applications, unstructured text data retrieval, etc., can solve the problems of poor use, unrealistic processing time, a large amount of time and processing capacity, etc.
Active Publication Date: 2017-03-01
INT BUSINESS MASCH CORP
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Problems solved by technology
However, methods known in the art for determining the similarity and dissimilarity of the data objects can be consuming, especially for large collections of data objects that correspondingly contain multiple "property values" or "attribute values" that need to be considered. Significant amount of time and processing power, since the methods are usually based on an "all-against-all" comparison of data objects, whereby multiple attribute values must be individually compared to each other
A common problem in the field of cloud computing is that if virtual machines or other program instances sharing the same set of hardware resources have too similar processing power or memory requirements, they will make poor use of said resources, such as because the processing power consumed may quickly reach the capacity limit of the resource while there may still be a large amount of unused memory
However, performing an all-to-all comparison of attributes of potentially thousands of large cloud computing environments to determine similar or dissimilar virtual machines is often not practical due to the complexity and processing time required for such comparisons
Method used
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Embodiment Construction
[0043] figure 1 is a flowchart of a method according to an embodiment of the present invention. Hereinafter, it will be stored by reference to such as figure 2 and Figure 4 The data objects of the storage medium 214 depicted in figure 1 method steps. In a first step 101 a plurality of data objects are provided. For example, data objects may be stored to storage medium 214, which is typically a storage volume containing one or more physical storage devices. A cluster computer, such as “cloud manager computer” 201 , can access storage medium 214 via network 213 . Alternatively, storage medium 214 may be part of cloud manager computer 201 . The method steps 102 - 105 are performed by the clustering module 206 of the cloud manager computer 201 . Data objects D01 - D19 may be provided to clustering module 206 by accessing storage medium 214 by clustering module or by receiving data objects by clustering module 206 via network 213 from another computer containing storage me...
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Embodiments of the present invention provide a method and system for identifying complementary data objects. In one aspect, the invention relates to a computer-implemented method for identifying complementary data objects. The method comprises: providing (101) a plurality of data objects (D01‑D16); applying (102) a clustering algorithm for grouping at least some of said data objects into two or more clusters (215‑217, 301 -303); for each cluster, computing (103) cluster centers (221-223, 310-313); computing (104) complementary cluster centers for at least a first of said cluster centers (310) (312); determine (105) the second cluster center (311) of the second cluster, this second cluster center is determined as the cluster center with the minimum distance about the complementary cluster center (312) among the cluster centers; select (106 ) at least one data object of the second cluster determined (316).
Description
technical field [0001] The present invention relates to the field of data processing, and more particularly to the field of clustering data objects. Background technique [0002] The problem of quickly determining the similarity and dissimilarity of data objects is a pervasive problem in the fields of data processing and data mining, and is relevant to a variety of technical applications. [0003] Depending on the respective use case scenario, combined processing of highly similar data objects, or alternatively, combined processing of highly dissimilar data objects may be advantageous. However, methods known in the art for determining the similarity and dissimilarity of the data objects can be consuming, especially for large collections of data objects that correspondingly contain multiple "property values" or "attribute values" that need to be considered. A great deal of time and processing power, since the methods are usually based on an "all-against-all" comparison of da...
Claims
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IPC IPC(8): G06F17/30
CPCG06F16/35G06F18/232
Inventor H·H·马达克里M·拉本斯金
Owner INT BUSINESS MASCH CORP