CLIAMS:WE CLAIM:

1. A method for facilitating analysis of a user behavior associated with usage of a communication device, the method comprising:
receiving, by a processor, unstructured data associated with one or more activities performed by a first user, wherein at least one activity is associated with usage of a communication device by the first user;
converting the unstructured data into a structured data by processing the unstructured data using one or more processing tools, wherein the processing comprises,
extracting a plurality of data fields associated with the unstructured data,
determining a plurality of keys and a plurality of values from the plurality of data fields,
mapping at least one key with the at least one value and
generating the structured data based upon the at least one key mapped with the at least one value; and
performing, by the processor, data analytics on the structured data in order to determine user behavior associated with the usage of the communication device by the first user.

2. The method of claim 1, wherein the communication device comprises at least one of a mobile phone, a smart phone, a cell phone, a personal computer, a laptop and a tablet.

3. The method of claim 1, wherein the unstructured data comprises usage of various applications installed on the communication device, call details, multimedia details, personal information of the first user and other relevant data related with the user behavior associated with a communication device.

4. The method of claim 1, wherein the unstructured data is received from one or more sources comprising sensors including an accelerometer tracking physical activity of the first user, a survey report, information received from the service providers, call detail log, message log, application log and internet.

5. The method of claim 1, wherein the one or more processing tools comprises Hadoop Distributed File System (HDFS), Big Data Analytics, MapReduce Framework, MPP database, and RapidMiner Framework.

6. The method of claim 1, wherein the analytics is performed based on one or more parameters, wherein the one or more parameters are predefined or are received from the second user.

7. A system for facilitating analysis of a user behavior associated with usage of a communication device, the system comprising:
a processor;
a memory coupled to the processor, wherein the processor is capable for executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising:
a receiving module to receive unstructured data associated with one or more activities performed by a first user, wherein at least one activity is associated with usage of a communication device by the first user;
a data processing module to
extract a plurality of data fields associated with the unstructured data,
determine at least one key and at least one value from the plurality of data fields,
map the at least one key with the at least one value and
generate the structured data based upon the at least one key mapped with the at least one value; and
a data analytics module to

perform data analytics on the structured data in order to determine user behavior associated with the usage of the communication device by the first user.

8. The system of claim 6, wherein the unstructured data comprises usage of various applications installed on the communication device, call details, multimedia details, personal information of the first user and other relevant data related with the user behavior associated with a communication device.

9. The system of claim 6, wherein the unstructured data is received from one or more sources comprising sensors including an accelerometer tracking physical activity of the first user, a survey report, information received from the service providers, call detail log, message log, application log and internet.

10. The system of claim 6, wherein the one or more processing tools comprises Hadoop Distributed File System (HDFS), Big Data Analytics, MapReduce Framework, MPP database, and RapidMiner Framework.

11. The method of claim 1, wherein the analytics is performed based on one or more parameters, wherein the one or more parameters are predefined or are received from the second user.

12. A non-transitory computer readable medium embodying a program executable in a computing device for facilitating analysis of a user behavior associated with usage of a communication device, the computer program comprising:
a program code for receiving unstructured data associated with one or more activities performed by a first user, wherein at least one activity is associated with usage of a communication device by the first user;
a program code for converting the unstructured data into a structured data by processing the unstructured data using one or more processing tools, wherein the processing comprises
a program code for extracting a plurality of data fields associated with the unstructured data,
a program code for determining a plurality of keys and a plurality of values from the plurality of data fields,
a program code for mapping the at least one key with the at least one value and
a program code for generating the structured data based upon the at least one key mapped with the at least one value; and

a program code for performing data analytics on the structured data based upon the one or more parameters in order to determine user behavior associated with the usage of the communication device by the first user.
,TagSPECI:
FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

METHOD AND SYSTEM FACILITATING ANALYSIS OF USER BEHAVIOR ASSOCIATED WITH USAGE OF A COMMUNICATION DEVICE

APPLICANT:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims no priority from any Indian patent application.

TECHNICAL FIELD
[002] The present disclosure described herein, in general, relates to a system and method of user behavior monitoring and particularly to a system and method for facilitating data analytics of user behavior associated with usage of a communication device.

BACKGROUND
[003] Understanding user behavior with respect to usage of smart communication devices is critical aspect from any marketing point of view. Examples of the smart communication devices used today may include smartphones, tablets, and phablets. These smart communication devices operate to some extent interactively and autonomously. According to survey reports, the smart communication devices penetration among mobile phone users globally has swiftly changed its gears and the mobile phone users are rapidly switching over to the smart communication devices. This is because the smart communication devices have become more affordable and have made advancement in technologies such as 3G and 4G networks. As the smart communication devices increases in large number, there exists an opportunity for service providers to provide better product and customized service offerings to the smart communication device users. The offerings in turn are dependent on the ways in which the smart communication devices are utilized by the users. Therefore, there is a need to monitor and/or track usage behavior/pattern of the smart communication devices by the users of the smart communication devices.
[004] Conventionally, the usage behavior/pattern of the smart communication devices is determined based on various surveys conducted over a specific sample space involving a very less number of smart communication device users. One of the major drawbacks of the conventional methods is that the analysis of the usage behavior/pattern is either group specific, or a location specific or a platform specific. For example, a survey is conducted corresponding to smart communication devices implementing the platforms like Android, Symbian, and Mac OS. Further, web analytics tools such as Google Analytics™ focus on analyzing the behaviour of website visitors. Furthermore, there exists an Android-based Activity recognition Application, using multiclass vector machine, which is used to identify the different physical activities performed by a user such as standing, walking, laying, walking upstairs and walking downstairs. The information about such actions or physical activity is retrieved from accelerometers but user has to wear smart device on the waist.
SUMMARY
[005] This summary is provided to introduce aspects related to systems and methods for facilitating analysis of a user behavior associated with usage of a communication device and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed disclosure nor is it intended for use in determining or limiting the scope of the claimed disclosure.
[006] In one implementation, a method for facilitating analysis of a user behavior associated with usage of a communication device is disclosed. The method may comprise receiving, by a processor, unstructured data associated with one or more activities performed by a first user. At least one activity is associated with usage of the communication device by the first user. The method may further comprise converting the unstructured data into a structured data by processing the unstructured data using one or more processing tools. The processing may further comprise extracting a plurality of data fields associated with the unstructured data, determining a plurality of keys and a plurality of values from the plurality of data fields, mapping at least one key with the at least one value and generating the structured data based upon the at least one key mapped with the at least one value. Furthermore, the method may perform data analytics on the structured data in order to determine user behavior associated with the usage of the communication device by the first user. In an embodiment, the analytics is performed based on one or more parameters, wherein the one or more parameters are predefined or are received from a second user.
[007] In another implementation, a system for facilitating analysis of a user behavior associated with usage of a communication device, the system comprising a processor and a memory coupled to the processor, wherein the processor is capable for executing a plurality of modules stored in the memory. The plurality of modules may comprise a receiving module configured to receive unstructured data associated with one or more activities performed by a first user, wherein at least one activity is associated with usage of a communication device by the first user. The system may comprise a data processing module configured to extract a plurality of data fields associated with the unstructured data, determine at least one key and at least one value from the plurality of data fields, map the at least one key with the at least one value, and generate the structured data based upon the at least one key mapped with the at least one value. Furthermore, the system may perform data analytics on the structured data in order to determine user behavior associated with the usage of the communication device by the first user. In an embodiment, the analytics is performed based on one or more parameters, wherein the one or more parameters are predefined or are received from a second user.
[008] In yet another implementation, a non-transitory computer readable medium embodying a program executable in a computing device for facilitating analysis of a user behavior associated with usage of a communication device, the computer program comprising a program code for receiving unstructured data associated with one or more activities performed by a first user, wherein at least one activity is associated with usage of a communication device by the first user. The computer program may further comprise a program code for converting the unstructured data into a structured data by processing the unstructured data using one or more processing tools. Furthermore, the computer program may comprise a program code for extracting a plurality of data fields associated with the unstructured data. The computer program may further comprise a program code for determining a plurality of keys and a plurality of values from the plurality of data fields. The computer program may further comprise a program code for mapping the at least one key with the at least one value, and a program code for generating the structured data based upon the at least one key mapped with the at least one value. The computer program may further comprise a program code for performing data analytics on the structured data in order to determine user behavior associated with the usage of the communication device by the first user. In an embodiment, the analytics is performed based on one or more parameters, wherein the one or more parameters are predefined or are received from a second user.

BRIEF DESCRIPTION OF THE DRAWINGS
[009] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[0010] Figure 1 illustrates a network implementation of a system for facilitating analysis of a user behavior associated with usage of a communication device, in accordance with an embodiment of the present disclosure.
[0011] Figure 2 illustrates the system, in accordance with an embodiment of the present disclosure.
[0012] Figure 3 illustrates general architecture of the system 102 facilitating usage analysis of the communication device by the user, in accordance with an embodiment of the present disclosure.
[0013] Figure 4 illustrates a Graphical User Interface (GUI) integrated with the system, in accordance with an embodiment of the present disclosure.
[0014] Figure 5 illustrates the GUI in accordance with an embodiment of the present disclosure.
[0015] Figure 6 illustrates analysis of a user behavior associated with usage of a communication device through the GUI, in accordance with an embodiment of the present disclosure.
[0016] Figure 7 illustrates a method for facilitating analysis of a user behavior associated with usage of a communication device, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0017] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0018] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0019] While aspects of the described system for facilitating analysis of a user behavior associated with usage of a communication device may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system. The system 102 may facilitate data analytics of a behaviour pertaining to use of a communication device by a first user. The communication device may comprise at least one of a mobile phone, a smart phone, a cell phone, a personal computer, a laptop and a tablet.
[0020] Although the present disclosure is explained considering that the system 102 is implemented as a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that the system 102 may be accessed by a second set of users, hereinafter referred to as a second user, through one or more user devices 104-1, 104-2…104-N, collectively also referred to as a user device 104, or the second user 104, hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[0021] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0022] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[0023] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0024] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0025] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks, functions or implement particular abstract data types. In one implementation, the modules 208 may include a receiving module 216, a data processing module 218, a data analytics module 220 and other module 222. The other module 222 may include programs or coded instructions that supplement applications and functions of the system 102.
[0026] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a database 212 and other data 214. The other data 214 may include data generated as a result of the execution of one or more modules in the other module 222.
[0027] In one implementation, at first, a second user may use the user device 104 to access the system 102 via the I/O interface 204. The second user may register themselves using the I/O interface 204 in order to use the system 102. The working of the system 102 using the plurality of modules 208 along with other components is explained in detail in following description.
[0028] Referring now to figure 3, in an embodiment, architecture 300 of the system 102 for facilitating analytics of usage of a communication device by a first user 302 (hereinafter referred to as user 302) is shown. As illustrated, the architecture 300 may further comprise the receiving module 216, the data processing module 218, the database 212, a Graphical User Interface (GUI) 306 and the data analytics module 220. These components of the architecture 300 are further explained in detail as below.
[0029] The receiving module 216 may be adapted to access unstructured data associated with of the one or more activities performed by the user 302. However, the unstructured data may be stored offline in the database 212 of the system 102, received from various sources such as sensors, data from survey reports, and data from service providers etc. The unstructured data may be associated with one or more activities performed by the user 302. The activities may comprise physical activities of the user 302, application usage of the user 302, call (incoming call and outgoing call) details, message (incoming message and outgoing message) details, multimedia (such as audio, video) usage of the user 302, internet browsing, and personal information about the user 302. The receiving module 216 may capture the unstructured data from one or more sources. The one or more sources may further comprise sensors including an accelerometer tracking physical activity of the user 302, various survey reports, information received from the service providers, call detail log, message log, application log, internet and the like. In one embodiment, the unstructured data may be processed by the data processing module 218 in order to reduce the unstructured data into a structured data. The unstructured data is reduced into the structured data by selecting a plurality of data fields useful for data analytics of user behavior associated with the communication device which is explained in detail in following description.
[0030] The data processing module 218 may be configured to process the unstructured data stored in the database 212 using a plurality of data processing tools (not shown in figure 3) including Apache™ Hadoop® Distributed File System (hereinafter referred as HDFS), Hadoop® MapReduce Framework (hereinafter referred as MapReduce framework), and the like. In an embodiment, for the purpose of processing the unstructured data, the unstructured data from the database 212 may initially uploaded into the HDFS. The system 102 may be enabled to upload the unstructured data into the HDFS through the GUI 306. The GUI 306 may also take path of a Hadoop directory. In another embodiment, the MapReduce Framework may be adapted to further process the unstructured data. The data processing module 218 may process the unstructured data explained in detail as below.
[0031] In an aspect, the MapReduce Framework may be enabled to extract the plurality of data fields from the unstructured data present in the database 212. The plurality of data fields may be selected based on usefulness of the plurality of data fields for further data analytics of user behavior associated with a communication device. The data processing module 218 may further be enabled to determine at least one key and at least one value from the plurality of data fields. Upon determination, the data processing module 218 may be enabled to map the at least one key with the at least value.
[0032] In yet another aspect, the information about the user 302 may be obtained from the service providers of the communication device, for example, from the purchase records available at the communication device distributer. The information about the user 302 may contain details like Name, Age, Date of Birth, Address, Occupation, and Email ID of the user 302. The data processing module 218 may be enabled to extract plurality of data fields required from analytics point of view at later stage.
[0033] Furthermore, in another aspect, the data associated with the call details for the user 302 may provide information about the calling behaviour of the user 302. The call details may contain numbers from or to which the calls were made i.e. outgoing calls, received calls i.e. incoming calls and missed calls. Further, the call details may comprise the timestamp at which the call entry was logged and the duration spent during the each of these calls. In an embodiment, to process the call details data, the call details data may be loaded into the HDFS using a Java Code. Thereafter, the MapReduce Framework may be adapted to get required fields such as USERID of the user 302, DIRECTION of the calls and DURATION of the calls. Further duplicate entries may be clubbed or grouped together based on particulars of the plurality of fields in the call details to reduce number of entries and data size.
[0034] In one embodiment, the message details may be analyzed to find out the number of messages or the size of messages sent by the user 302. The message details may contain plurality of data fields including a message sent (i.e. an outgoing message), a message received (i.e. an incoming message), size of the message and a timestamp. To process the message details the data processing module 218 may be enabled to first load the message details into the HDFS using Java Code. Then, the MapReduce Framework may be configured to extract plurality of data fields like USERID, DIRECTION of the messages and DURATION. Furthermore, mapping may also be performed after determining at least one key and at least one value form the plurality of data fields selected from the message details. Similarly, in another aspect, the application usage details may be reduced to the structured data. For the application usage details, the plurality of data fields may be selected as USERID, APP and DURATION.
[0035] In yet another aspect, the physical activities of the user 302 may be tracked using the accelerometer sensor that returns a real valued estimate of acceleration along the x, y and z axes from which velocity and displacement of the user 302 may also be estimated. The MapReduce Framework may be enabled to select the plurality of data fields from the accelerometer details. The plurality of data fields selected from the accelerometer details comprising time, acceleration along x axis, acceleration along y axis and acceleration along z axis. Furthermore, to process the physical activity details, the details obtained from all three axes may be loaded into the HDFS. Thereafter, the MapReduce Framework may be enabled to extract the plurality of data fields like USERID, ACTIVITY and DURATION. After that a Java™ Archive (JAR) file for the physical activity details may be created from the MapReduce Framework. The MapReduce Framework may further be enabled to map based on the USERID, ACTIVITY as key and DURATION as value. In the reduce phase for each key USERID, ACTIVITY, the corresponding value of DURATION is added. In one example, the user 302 carrying the Android phone in his/her front pants leg pocket and was asked to walk, jog, ascend stairs, descend stairs, sit, and stand for specific periods of time. An application “ActiTracker” installed on the communication device of the user 302 enabled to capture data associated with different physical activities performed by the user 302.
[0036] Similarly, in yet another aspect, the MapReduce Framework may enable to extract the plurality of data fields from the Media usage details. The plurality of data fields may USERID, MEDIA and DURATION. The data processing module 218 may be configured to map based on the USERID, the MEDIA as key and the DURATION as value. In the reduce phase for each key USERID, MEDIA, the corresponding value of DURATION is added. Upon reducing the unstructured data to the structured data using the MapReduce Framework, the structured data obtained may be further processed using the one or more processing tools as explained below.
[0037] The structured data may contain those data fields that are required for data analytics at final stage. In an embodiment, the structured data may contain separate tables for each of the one or more activities performed by the user 302. The structured data may be stored in the database 212. Further, in next step all the tables created by the MapReduce Framework may be modified to a single table comprising all the data fields from each single table created, by the MapReduce Framework, and stored in the database 212. Upon reducing the unstructured data into the structured data, the system 102 may be enabled to perform the data analytics of user behavior using the data analytics module 220.
[0038] The data analytics module 220 may be configured to analyze the structured data using a plurality of data analytics tools (not shown in figure 3) including RapidMiner Analytics Framework, K-means Algorithm, Massively Parallel Processing (MPP) database such as Greenplum® MPP database. In one aspect, the data analytics module 220 may create a data analytics model using the RapidMiner Analytics Framework based on the structured data. Furthermore, in an aspect, a data analytics workflow may be created using the operators such as a Read Database operator, a K-means Algorithm and an Aggregate Operator to build the data analytics model. The analytics workflow may be enabled to find percentage of the physical activities, the applications usage, the multi-media usage and the count of sample space based on the user selected variables. In another embodiment, the structured data connection may be established for the data analytics model so that the structured data may be fed dynamically as input to the data analytics model. The system 102 may be further enabled to generate a view created from the structured data which may extract the required plurality of data fields from structured data based on the second user selections. The second user 104 may, via the GUI 306, input one or more parameters required for performing data analytics. In an embodiment, the system 102 receive predefined one or more parameters for data analytics. In yet another embodiment, the second user 104 may input the one or more parameters for data analytics.
[0039] In one embodiment, the one or more parameters may be associated with the data analytics. Further, the K-means algorithm may be applied on the structured data and clusters may be generated for the data analytics model created by the data analytics module 220. The K-means algorithm may also find the number of items in each of the clusters created by the K-means algorithm based on the structured data provided to the k-means algorithm. The clusters generated by the k-means algorithm may further be passed to an aggregate function in order to find the average of clusters generated.
[0040] In an embodiment, the second user 104, accessing the system 102, may be an analyst, a stakeholder related to the communication device field or any other person interested in performing data analytics. The GUI 306 may facilitate selection, by the second user 104, the one or more parameters associated with the data analytics. In an embodiment, the one or more parameters may be predefined in the system 102 or selected by the second user 104. The system 102 may be configured to integrate the GUI 306 with the RapidMiner Framework. Additionally, the system 102 may be configured to execute the data analytics workflow through the GUI 306. To execute the data analytics workflow, the dynamically generated views of the structured data may be provided.
[0041] The data analytics may be performed using the Greenplum® MPP database. The data analytics module 220 may be adapted to analyze the dynamically generated view of the structured data. Based on the one or more parameters, views of the structured data may be created dynamically. Furthermore, the views of the structured data may not only pass the required data to the data analytics workflow but also reduce the use of the one or more parameters and set role operators while creating the data analytics workflow. In next step, result comprising the percentage and the count based on the one or more parameters may be displayed to the second user through the GUI 306. The data analytics result may be configured by following few steps such as a) map the one or more data fields with the structured data, b) reading the input for the one or more parameters, c) establishing the connection between the structured, views of the structured data, and the GUI 306, and d) displaying the result of the data analytics. The system 102 may be explained in detail by illustrating few screenshots of the GUI 306 comprising of selection by the second user and display of the data analytics result through figures 4, 5 and 6.
[0042] Figure 4 illustrates a screenshot of the GUI 306 displaying the one or more parameters comprising Gender, Age, Location, Occupation, Physical Activity, Application Usage, and Multimedia Usage, and the like. Figure 5 illustrates another screenshot of the GUI 306 after the selection of the one or more parameters. The system 102 may perform the data analytics in response to click action performed by the second user on a tab ‘Analyze’ present on the GUI 306. In one aspect, corresponding results of the data analytics may be displayed through the GUI 306. The screenshot of the data analytics result is shown in figure 6. The numerical figures in the figure 6 represent both the percentage and the count for the one or more parameters selected.
[0043] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0044] Some embodiments of the present disclosure allow analyzing patterns in which the attention of the users is spread across various applications of similar types and types of actions and physical activities performed by the users.
[0045] Some embodiments of the present disclosure that allow analyzing the user behavior associated with the communication device and further categorizes the communication device users based on the parameters such as age group, gender, occupations, and locations.
[0046] Some embodiments of the present disclosure facilitate target based service rendering to the communication device users.
[0047] Some embodiments of the present disclosure facilitate data analytics of the user associated with the communication device without wearing/carrying any additional devices or sensors on the user body.
[0048] The order in which the method 700 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 700 or alternate methods. Additionally, individual blocks may be deleted from the method 700 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 700 may be considered to be implemented in the above described system 102.
[0049] At block 702, unstructured data associated with one or more activities performed by a user may be received by the receiving module 216. However, the unstructured data may be stored offline in the database 212 of the system 102 received from various sources such as sensors, data from survey reports, and data from service providers etc. Furthermore, at least one activity is associated with usage of a communication device by the user, and wherein the unstructured data is received from one or more sources.
[0050] At block 704, the unstructured data may be processed using a MapReduce Framework to extract a structured data from the unstructured data. The processing may further comprise extracting a plurality of data fields associated with the unstructured data using the MapReduce Framework. Further, processing comprise determining at least one key and at least one value from the plurality of data fields. Finally, the processing comprises mapping the at least one key with the at least one value to generate the structured data based upon the at least one key mapped with the at least one value. Further, processing enables creating of data analytics model in order to perform data analytics on the structured data.
[0051] At block 706, one or more parameters may be received, through a Graphical User Interface (GUI) 306. The one or more parameters are associated with the data analytics. The data analytics model created at block 704 may further be utilized to create various views of the structured data based upon the one or more parameters.
[0052] At block 708, the data analytics may be performed using the structured data based on the one or more parameters.
[0053] Although implementations for methods and systems for facilitating analysis of a user behavior associated with usage of a communication device have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described.