CLIAMS:CLAIMS

1. A method of sorting a plurality of objects by calculating a sortable hash value for each of said plurality of objects, said method comprises:

fetching information on at least one of a plurality of properties associated with each of said plurality of objects;
calculating property hash value for each of said plurality of properties associated with each of said plurality of objects;
defining an object hash value for each of said plurality of objects based on said property hash values of said properties associated with each of each of said plurality of objects; and
sorting said plurality of objects based on said defined object hash values.

2. The method as in claim 1, wherein said information on said pluralities of properties are pre-configured by a user.

3. The method as in claim 1, wherein said property hash value is specific to each property.

4. The method as in claim 1, wherein said property hash value for each of said plurality of properties is calculated based on at least one of an accuracy, user preference and a priority value assigned to each of said plurality of properties.

5. The method as in claim 4, wherein said accuracy, user preference and a priority value are pre-configured by a user.

6. The method as in claim 1, wherein said object hash value for each of said plurality of objects is defined by summing said calculated property values of properties associated with each of said plurality of objects.

7. A system of sorting a plurality of objects by calculating a sortable hash value for each of said plurality of objects, said system configured for:

fetching information on at least one of a plurality of properties associated with each of said plurality of objects using an object hash based identifier system;
calculating property hash value for each of said plurality of properties associated with each of said plurality of objects using said object hash based identifier system;
defining an object hash value for each of said plurality of objects based on said property hash values of said properties associated with each of each of said plurality of objects using said object hash based identifier system; and
sorting said plurality of objects based on said defined object hash values using an object hash based identifier system.

8. The system as in claim 7, wherein said object hash based identifier system provides means for preconfiguring said information on said pluralities of properties using an input module.

9. The system as in claim 1, wherein said object hash based identifier system is further configured to calculate said property hash value specific to each property using a hash defining module.

10. The system as in claim 1, wherein said object hash based identifier system is configured to calculate said property hash value for each of said plurality of properties based on at least one of an accuracy, user preference and a priority value assigned to each of said plurality of properties using a hash defining module.

11. The system as in claim 10, wherein said object hash based identifier system provides means for preconfiguring value of said accuracy, user preference and priority value using an input module.

12. The system as in claim 7, wherein said object hash based identifier system is further configured to define said object hash value for each of said plurality of objects by summing said calculated property values of properties associated with each of said plurality of objects using a hash defining module.

Dated : 21st June, 2013 Signature
Vikram Pratap Singh Thakur
,TagSPECI:
FORM 2
The Patent Act 1970
(39 of 1970)
&
The Patent Rules, 2005
COMPLETE SPECIFICATION
(SEE SECTION 10 AND RULE 13)

TITLE OF THE INVENTION
“IDENTIFYING OBJECTS USING ‘OBJECT HASH’ DERIEVED FROM OBJECT PROPERTIES”
APPLICANTS:
Name : HCL Technologies Limited
Nationality : Indian
Address : HCL Technologies Ltd., 50-53 Greams
Road, Chennai – 600006, Tamil Nadu, India

The following Specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed:
TECHNICAL FIELD
[001] The embodiments herein relate to database management systems and, more particularly, to identifying/grouping similar objects quickly using a sortable hash that is derived from different properties of the object.

BACKGROUND
While managing large set of data one of the major challenges that we face is identifying/grouping similar objects from available large data set. These processes involve conventional methods such as verifying all the available information which are more time consuming especially when the available database is huge. Algorithms and methods that exists today for ‘identification of similar objects’ , ‘Group the similar objects’, ’indexing for search’ or ‘measurement of similarity between two objects’ are high costly in terms of CPU usage, I/O and memory usage. Further it is not affordable when we need to do operations in a device with limited processing power or to do this in a large set of data (ex: big data analysis). Further more, storage is going to be another problem, as device requires lot of storage space to store all object databases.
[002] What is needed therefore is a system and method which maps everything to a single dimension and sorts the data in particular order so that similar objects can be identified easily from the sorted list.

SUMMARY

[003] In view of the foregoing, an embodiment herein provides a method of sorting a plurality of objects by calculating a sortable hash value for each of the plurality of objects, the method comprises fetching information on at least one of a plurality of properties associated with each of the plurality of objects; calculating property hash value for each of the plurality of properties associated with each of the plurality of objects; defining an object hash value for each of the plurality of objects based on the property hash values of the properties associated with each of each of the plurality of objects; and sorting the plurality of objects based on the defined object hash values.
[004] Embodiments further disclose a system of sorting a plurality of objects by calculating a sortable hash value for each of the plurality of objects, the system configured for fetching information on at least one of a plurality of properties associated with each of the plurality of objects using an object hash based identifier system; calculating property hash value for each of the plurality of properties associated with each of the plurality of objects using the object hash based identifier system; defining an object hash value for each of the plurality of objects based on the property hash values of the properties associated with each of each of the plurality of objects using the object hash based identifier system; and sorting the plurality of objects based on the defined object hash values using an object hash based identifier system.
[005] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES
[006] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[007] FIG. 1 illustrates a block diagram of object hash based identifier system, as disclosed in the embodiments herein; and
[008] FIG. 2 is a flow diagram which shows various steps involved in the process of identifying/grouping the similar objects using sortable hash that is derived from different properties of the object, as disclosed in the embodiments herein.

DETAILED DESCRIPTION OF THE INVENTION
[009] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0010] The embodiments herein disclose a system and method to identify/group the similar objects quickly by using a sortable hash which is derived from different properties of the object. Referring now to the drawings, and more particularly to FIGS. 1 through 2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
[0011] FIG. 1 illustrates a block diagram of object hash based identifier system, as disclosed in the embodiments herein. The system comprises of an input module 101, a hash defining module 102, a sorting module 103 and a memory module 104.
[0012] Primarily, the user has to identify the objects and their properties based on his/her interest. The term ‘object’ may refer to any entity that may be identified using its properties. For example, a computer with properties like hard-ware platform, operating system and memory size, a human with properties like height, weight, skin color, a customer with his demographic properties and so on. Further, for each property identified, maximum and minimum values can be assigned based on user requirements. For example for a person’s age 100 and 18 can be considered as maximum and minimum values corresponding to the property. Further more, accuracy can also be defined for each object based on user preferences. Later, user may give the required input parameters such as maximum and minimum values of each property, accuracy and so on to the object hash based identifier system 100 by using any suitable Input/Output user interface such as a computer, tablet, smart phone and so on. The input module 101 fetches this input information from user device and send to Hash defining module 102 for further processing.
[0013] Hash defining module 102 of object hash based identifier system 100 defines ‘object hash’ value for each identified object. In order to define ‘object hash’ value, Hash defining module 102 fetches required input parameters from input module 101.On receiving input parameters from input module 101, hash defining module 102 calculates property hash values for each property of an object by mapping each property into a particular number between 0 and accuracy value. For example, for an object if the accuracy value is defined as 10, all the properties of that object are mapped to the integer values between 0 to 9. Object properties may be of two types. First type relates to properties which take continuous values, for example if we take body temperature as a property, it may take infinite values. The second type of properties may take discrete/categorical values, for example if we take age of a person as a property, it may take finite values. Depending on the type of property (whether the property takes continuous or discrete values), property hash can be defined for a particular property by using following methods.
[0014] Hash value for a property which take continues values, may be calculated by using any of the following methods:
a) Property hash = nearest integer of ------------- (1)
where,
max_value, min_value defines the maximum and minimum values of a particular property respectively.
Value defines the specific property value through which specific objects may be searched.
accuracy defines maximum value of hash that can be given to a property
[0015] For example, for an object ‘person’, ‘age’ can be taken as a property, then the maximum and minimum values can be taken as 100 and 18 respectively. Value may be any desired age between 18 and 100. Accuracy can be any pre-defined value so to map property hash values up to this maximum value. If accuracy is defined as 10, all the properties of that object are mapped to the values between 0 to 9.
b) Using a ‘range to hash’ map created using its frequency distribution or probability of occurrence.
[0016] If we consider the person-age example, let us take that most of the persons are in age between 20 to 50. Hence the range to hash map is created by giving more importance to mostly repeated or high probability range. The following table illustrates the example more:

Range Hash value
0 – 20 0
21 – 25 1
26 – 30 2
31 – 35 3
36 – 40 4
41 - 45 5
46 – 50 6
51 – 60 7
61 – 80 8
81 and above 9
Table 1
[0017] Thus by using ‘range to hash’ mapping, hash values can be defined for a property. Further, while mapping ‘outliners’ are excluded to make sure that hash value is not affected. For example, in the above example, if there is a person with age 110 we can consider this as an exception but not as the maximum value in order to calculate hash.
c) Using a ‘range to hash’ map created manually to highlight the property values that are important for the context.
[0018] In this method, the properties which are important for the context are highlighted. For example, while considering person-age, if the interested age group is between 30 to 40, the mapping may be as follows:
Age between 30 to 40 is mapped with hash values from 1 to 8
If age < 30, hash value is mapped to 0
If age > 40, hash value is mapped to 9
[0019] Hash value for a property which take discreet/categorical value, may be calculated by using any of the following methods, after placing the values in a list in sorted order i.e., more similar values should be placed nearby. For example, let us consider ‘computer’ as an object to which ‘Operating System (OS)’ is one of its property. Assume that the property OS has the values like ‘DOS’, ‘Win7’, ‘WinXP’, Win95, ‘Win3.1’, ‘Win8’ and so on. These values may be sorted as DOS, Win3.1, Win95, WinXP, Win7, and Win8. Now property hash can be given as:
a) Property hash = nearest integer of ------------- (2)
where,
position_of_value_in_the_list defines the position of the specific property in the list of properties.
length_of_list defines the total length of list which is related the specific property.
accuracy defines maximum value of hash that can be given to a property
From the sorted list, position_of_value_in_the_list is given depending on position of desired Operating System in the list and length_of_list indicates the total length of the list.
b) Using a ‘value to hash’ map created using its frequency distribution or probability of occurrence.
When we consider the above example (which is mentioned in method (a)) with accuracy 4, the mapping can be done as follows:
As we know that DOS, Win3.1, WinXP occur very less in the property list,
DOS, Win3.1, Win95 can be mapped to hash value 0
WinXP can be mapped to hash value1
Win7 can be mapped to hash value 2 and
Win 8 can be mapped to hash value 3
Further, while mapping ‘outliners’ are excluded to make sure that they would not influence the hash value.
c) Using a ‘value to hash’ map created manually to highlight the property values that are important for the context. In this method, the properties which are important for the context are highlighted.
[0020] Further, properties that are having same significance can be mapped to a single hash value by calculating its weighted mean. For example, if two properties say P1 and P2 have same significance, the total hash value can be given as the weighted average of those two hash values.
[0021] After calculating hash values for each property of a particular object, hash defining module 102 defines total hash value of that object by using property hash values and accuracy defined.
[0022] Hash of an objects can be calculated using following formula
Object Hash = --------------------- (3)
i.e. Object Hash = ((b(n-1)).h1)+((b(n-2)).h2)+((b(n-3)).h3 …… +((b(n-n)).hn)
Where,
b defines accuracy (i.e., if the accuracy that we are looking for is 10, we shall map the properties to a value between 0 and 10)
n defines number of properties
h1 defines property hash from the value of the most significant property (a value between 0 to b-1)
hn defines property hash from the value of the most significant property (a value between 0 to b-1)
[0023] In an embodiment, object hash can also be calculated by mapping range of bits of a hash to corresponding property hashes. Position of bits may vary based on how multi byte numbers are stored in the device/computer (big-endian or little-endian) i.e., positioning has to make sure that the constructed hash value is sortable. In this method, the accuracy (b) can vary from property to property based on number of bits allocated to each. bn = 2 (number of bits of hn)
[0024] For example, let us consider an object with five properties p1, p2, p3, p4 and p5. These five properties can be assigned to different accuracy levels depending on the importance. Let h1, h2, h3, h4 and h5 be the property hashes respectively.
b16 b15 b14 b13 b12 b11 b10 b9 b8 b7 B6 b5 b4 b3 B2 b1
h1 h2 h3 h4 h5
Table 2
h1 defines property hash from the value of the most significant property (a value between 0 to 23-1)
h2 defines property hash from the value of the next significant property (a value between 0 to 22-1)
h3 defines property hash from the value of the next significant property (a value between 0 to 24-1)
h4 defines property hash from the value of the next significant property (a value between 0 to 24-1).
h5 defines property hash from the value of the least significant property (a value between 0 to 23-1)
Finally ‘object hash’ of the object is given by considering the total bits of all property hashes.
[0025] After defining object hashes for each object, sorting module 103 sorts all the objects based on defined hash values. This can be done by using any known algorithm. Further, the sorting module 103 makes sure that similar objects are placed in adjacent positions as they have hash values nearer to each other. This makes identifying/grouping the similar objects easier as they are at near by positions. Later, the sorted list of hash values is stored in memory module 104 for future reference. As the memory module 104 stores only the list hash values of the objects instead of whole object properties, limited memory is sufficient which increases efficiency of the system.
[0026] FIG. 2 is a flow diagram which shows various steps involved in the process of identifying/grouping the similar objects using sortable hash that is derived from different properties of the object, as disclosed in the embodiments herein. In order to identify similar objects from a set of objects, firstly a sorted list has to be prepared for the objects by using their hash values. The object hash based identifier system 100 fetches (202) information on the objects of user’s interest along with their properties. The object hash based identifier system 100 may provide a suitable user interface for the user to enter information on the identified input parameters such as maximum and minimum values of each property, accuracy required for an object and so on. Further, Hash defining module 102 fetches these inputs from input module 101 in order to define ‘object hash’ for each identified object. Generally, each object may have different properties. For example, if we take computer as an object, properties may be operating system, platform, RAM capacity, patch level and so on. To get ‘object hash’ of a particular object, ‘property hash’ value of each property is calculated first i.e. if we consider above example, ‘object hash’ of computer can be defined by calculating individual ‘property hash’ values of its parameters such as operating system, platform, RAM capacity and so on. Based on the type of property of an object (whether property takes continuous or discrete values), the hash defining module 102 calculates (204) its ‘property hash’ values. Hash value for a property which take continues values, may be calculated by using equation (1). For the properties which take discrete/categorical values, a sorted list of similar properties is made before calculating ‘property hash’ for a particular property. Later ‘property hash’ can be calculated by using formula (2). In an embodiment, ‘property hash’ can also be calculated by using one of the mapping method (either ‘range to hash’ or ‘value to hash’) described above. Further, properties that are having same significance can be mapped to a single hash value by calculating its weighted mean. After calculating ‘property hash’ values, the hash defining module 102 defines (206) the ‘object hash’ by using equation (3). In an embodiment, object hash can also be calculated by mapping range of bits of hash to corresponding property hashes.
[0027] Further, the sorting module 103 sorts (208) the objects based on defined ‘object hash’ values and prepares (210) a sorted list by using any known algorithm. This sorted list is stored in memory module 104 of object hash based identifier system. When a user wants to identify similar objects related to particular object, he/she uses this sorted list as reference. Firstly ‘object hash’ of that particular object is calculated by using suitable method (which are described above). Further, this hash value is compared with the hash values of the sorted list. The values which are nearer to the calculated hash value are identified. Finally, corresponding object details are noted from the list. Thus the similar objects can be identified easily by using sorted hash list.
[0028] For example, let us take computer as an object. In order to identify similar computers, the sorted list of computers has to be papered. The properties of this object may be Operating system, platform, RAM capacity, HDD and patch level. After identifying properties, ‘property hash’ values of each property is calculated. Then ‘object hash’ can be defined by using equation (3). Following data illustrates how this has can be used it identify similar computers. Here, the object hash is calculated using b=16
Unsorted list of computers:
ID OS h1 Platform h2 RAM h3 HDD h4 Patch Level h5 Object hash
1 Vista 2 AMD-64 Bit 3 6Gb 4 7Tb 15 SP3 3 144627
2 Win8 4 AMD-64 Bit 3 15Gb 13 2Tb 10 SP1 1 277921
3 Win95 0 Intel 0 2Gb 1 900Gb 8 - 0 384
4 WinXP 1 AMD 1 9Gb 7 700Gb 5 SP1 1 71505
5 Win7 3 Intel 0 6Gb 4 6Tb 14 - 0 197856
6 Win8 4 Intel 0 6Gb 4 6Tb 14 - 0 263392
7 Win8 4 Intel 0 12Gb 10 7Tb 15 SP1 1 264945
8 Win8 4 Intel 0 13Gb 11 250Gb 1 SP1 1 264977
9 Win95 0 AMD 1 17Gb 15 6Tb 14 SP1 1 8161
10 WinXP 1 Intel 0 1Gb 0 750Gb 6 SP1 1 65633
11 Win7 3 AMD 1 3Gb 2 2Tb 10 SP3 3 201379
12 Win95 0 AMD 1 7Gb 5 900Gb 8 SP2 2 5506
13 Vista 2 Intel-64bit 2 12Gb 10 100Gb 0 SP1 1 141825
14 Win7 3 AMD-64 Bit 3 17Gb 15 800Gb 7 SP3 3 212851
15 Win95 0 AMD 1 12Gb 10 100Gb 0 - 0 6656
16 Win8 4 AMD-64 Bit 3 13Gb 11 750Gb 6 SP2 2 277346

After performing sorting, the list may become as follows:
ID OS h1 Platform h2 RAM h3 HDD h4 Patch Level h5 Object hash
3 Win95 0 Intel 0 2Gb 1 900Gb 8 - 0 384
12 Win95 0 AMD 1 7Gb 5 900Gb 8 SP2 2 5506
15 Win95 0 AMD 1 12Gb 10 100Gb 0 - 0 6656
9 Win95 0 AMD 1 17Gb 15 6Tb 14 SP1 1 8161
10 WinXP 1 Intel 0 1Gb 0 750Gb 6 SP1 1 65633
4 WinXP 1 AMD 1 9Gb 7 700Gb 5 SP1 1 71505
13 Vista 2 Intel-64bit 2 12Gb 10 100Gb 0 SP1 1 141825
1 Vista 2 AMD-64 Bit 3 6Gb 4 7Tb 15 SP3 3 144627
5 Win7 3 Intel 0 6Gb 4 6Tb 14 - 0 197856
11 Win7 3 AMD 1 3Gb 2 2Tb 10 SP3 3 201379
14 Win7 3 AMD-64 Bit 3 17Gb 15 800Gb 7 SP3 3 212851
6 Win8 4 Intel 0 6Gb 4 6Tb 14 - 0 263392
7 Win8 4 Intel 0 12Gb 10 7Tb 15 SP1 1 264945
8 Win8 4 Intel 0 13Gb 11 250Gb 1 SP1 1 264977
16 Win8 4 AMD-64 Bit 3 13Gb 11 750Gb 6 SP2 2 277346
2 Win8 4 AMD-64 Bit 3 15Gb 13 2Tb 10 SP1 1 277921

[0029] Depending on the hash values, computers are sorted. Depending on hash values we can easily identify the similar objects. For example, if the user needs similar computers for a reference computer whose hash value is 144500.He/she can easily identify from the above sorted list.
[0030] The various actions in method 200 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 2 may be omitted.
[0031] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in Fig. 1 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.
[0032] The embodiment disclosed herein specifies a system for managing database. The mechanism allows easy identification/clustering of similar objects by preparing sorted list of objects defined in terms of their hash values providing a system thereof. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The method is implemented in a preferred embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof, e.g. one processor and two FPGAs. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means are at least one hardware means and/or at least one software means. The method embodiments described herein could be implemented in pure hardware or partly in hardware and partly in software. The device may also include only software means. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0033] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the claims as described herein.