Title of invention:
CONNECTED VEHICLE ANALYTICS FRAMEWORK IN SEAMLESS INTEGRATION WITH DISPARATE CONNECTED VEHICLE PLATFORM DATA SOURCES

Applicant:
HCL Technologies Limited
A Company incorporated in India under the Companies Act, 1956
having address:
B-39, Sector 1, Noida - 201 301,
Uttar Pradesh, India

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS REFERENCE TO RELATED APPLICATIONS
[001] This patent application does not claim priority from any application.

TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information.

BACKGROUND
[003] In an era of Internet of Things (IoT), where huge amount of data is generated by a plurality of IoT devices and various kinds of analysis needs to be performed on such data for the betterment of the society. One of the applications of the IoT is connected vehicle systems. The connected vehicle systems enable the plurality of IoT devices, deployed on a vehicle, to generate the data. Upon generation, the data may be streamed onto a backend server side platform for analysis using functional use cases.
[004] While the huge amount of the data is being aggregated onto the backend server side platform, it’s an imperative need to determine insight of the data in order to deduce meaningful information. To do so, each vehicle manufacturer needs to have their respective analytics framework to perform the analysis as the analytics framework is not an integral part of the connected vehicle systems. This certainly creates a complexity for third party agencies that require the meaningful information and thus becomes a challenge, for the third party agencies, to collate the data being received from different vehicle manufacturers and thereby perform analysis on such data.

SUMMARY
[005] Before the present systems and methods, are described, it is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and methods for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[006] In one implementation, an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information is disclosed. The analytics framework may comprise a processor and a memory coupled to the processor. The processor may execute a plurality of modules present in the memory. The plurality of modules may comprise a data integration module, an analytics model repository creation module, a parameter configuration module, and a data analytics module. The data integration module may integrate each vehicle platform database, corresponding to a vehicle manufacturer, with a central database. In one aspect, each vehicle platform database may store data in a native data format. In one aspect, the data comprises a plurality of parameters. The data integration module may populate the data stored in each vehicle platform database into the central database. The analytics model repository creation module may enable a user to create an analytics model repository comprising a plurality of use-case models. In one aspect, each use-case model may be configured to perform a predefined analysis. The parameter configuration module may configure one or more parameters, of the plurality of parameters, with each use-case model. The one or more parameters may be configured based on meaningful information expected from each respective use-case model. The data analytics module may perform analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models. The analytics may be performed on the one or more parameters in order to deduce meaningful information.
[007] In another implementation, a method for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information is disclosed. In order to perform analytics on the data, initially, each vehicle platform database, corresponding to a vehicle manufacturer, may be integrated with a central database. In one aspect, each vehicle platform database may store data in a native data format. In one aspect, the data may comprise a plurality of parameters. Subsequent to the integration of each vehicle platform database with the central database, the data stored in each vehicle platform database may be populated into the central database. After populating the data, a user may be enabled to create an analytics model repository comprising a plurality of use-case models. In one aspect, each use-case model may be configured to perform a predefined analysis. Once the analytics model repository is being created, one or more parameters, of the plurality of parameters, may be configured with each use-case model. The one or more parameters may be configured based on meaningful information expected from each respective use-case model. Subsequent to the configuration of the one or more parameters, analytics may be performed on the one or more parameters associated with a use-case model, of the plurality of use-case models. In one aspect, the analytics may be performed on the one or more parameters in order to deduce meaningful information. In one aspect, the aforementioned method for facilitating the analytics framework for connected vehicles may be performed by a processor using programmed instructions stored in a memory.
[008] In yet another implementation, non-transitory computer readable medium embodying a program executable in a computing device for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information is disclosed. The program may comprise a program code for integrating each vehicle platform database, corresponding to a vehicle manufacturer, with a central database, wherein each vehicle platform database stores data in a native data format, and wherein the data comprises a plurality of parameters. The program may further comprise a program code for populating the data stored in each vehicle platform database into the central database. The program may further comprise a program code for enabling a user to create an analytics model repository comprising a plurality of use-case models, wherein each use-case model is configured to perform a predefined analysis. The program may further comprise a program code for configuring one or more parameters, of the plurality of parameters, with each use-case model, wherein the one or more parameters are configured based on meaningful information expected from each respective use-case model. The program may further comprise a program code for performing analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models, wherein the analytics is performed on the one or more parameters in order to deduce meaningful information.
BRIEF DESCRIPTION OF THE DRAWINGS
[009] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, example constructions of the disclosure is shown in the present document; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and the drawings.
[0010] The detailed description is given 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.
[0011] Figure 1 illustrates a network implementation of an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information, in accordance with an embodiment of the present subject matter.
[0012] Figure 2 illustrates the analytics framework, in accordance with an embodiment of the present subject matter.
[0013] Figure 3 illustrates an example, in accordance with an embodiment of the present subject matter.
[0014] Figure 4 illustrates a method for facilitating an analytics framework for connected vehicles to perform analytics on the data, captured from the disparate connected vehicles, in order to deduce meaningful information, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION
[0015] 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 as used herein and in the appended claims, 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.
[0016] 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.
[0017] The present invention addresses the need of augmenting connected vehicle analytics framework on top of backend connected vehicle platforms. This facilitates a separate and independent analytics framework that may be seamlessly integrated with a plurality of backend connected vehicle platforms or connected vehicle platform data sources. In order to determine the significance of the connected vehicle analytics framework, consider an example where each vehicle manufacturer has its respective vehicle platform database, present in the backend connected vehicle platform, capable of storing data being received from a plurality of sensors and/or IoT devices deployed on the vehicle. It may be understood that the data received may be stored in a native data format in the respective vehicle platform databases. Since there are numerous vehicle manufacturers thus the data being generated may be in distinct formats and hence may not be feasible for an analytics platform, connected with the plurality of backend connected vehicle platforms, to perform analytics on the data being received in distinct formats and thus may not deduce meaningful information.
[0018] The connected vehicle analytics framework facilitates to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information. It may be understood that the data may be captured in distinct formats from disparate connected vehicles and stored in respective vehicle platform databases. Once the data is stored, the connected vehicle analytics framework integrates each vehicle platform database, corresponding to a vehicle manufacturer, with a central database present in the connected vehicle analytics framework. Subsequently the data stored in each vehicle platform database is populated into the central database.
[0019] The connected vehicle analytics framework may further comprise an analytics model repository. The analytics model repository may store a plurality of use-case models for usable/standard use cases. In one aspect, each use-case model may be configured to perform a predefined analysis. Examples of the analysis performed by the plurality of use-case models may include, but not limited to, driving performance analysis, vehicle engine performance analysis, and fuel performance analysis. In order to perform at least one of the aforementioned analyses, one or more parameters, of the plurality of parameters, may be configured with each use-case model. Based on the one or more parameters, each use-case model may perform analytics and thereby deduce meaning information that may be used to take corrective measures, if required.
[0020] Thus the connected vehicle analytics framework may enable vehicle analytics to be augmented to the backend connected vehicle platforms. In one embodiment, the meaningful information may be displayed on a dashboard as per the analytical format desired by a user. While aspects of described system and method for facilitating an analytics framework performing analytics on data, captured from the disparate connected vehicles, in order to deduce meaningful information and may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context connected vehicles.
[0021] Referring now to Figure 1, a network implementation 100 of an analytics framework 102 for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information is disclosed. In order to perform analytics on the data, initially, the analytics framework 102 integrates each vehicle platform database, corresponding to a vehicle manufacturer, with a central database. Subsequent to the integration of each vehicle platform database with the central database, the analytics framework 102 populates the data stored in each vehicle platform database into the central database. After populating the data, the analytics framework 102 enables a user to create an analytics model repository comprising a plurality of use-case models. In one aspect, each use-case model may be configured to perform a predefined analysis. Once the analytics model repository is being created, the analytics framework 102 configures one or more parameters, of the plurality of parameters, with each use-case model. Subsequent to the configuration of the one or more parameters, the analytics framework 102 performs analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models. In one aspect, the analytics may be performed on the one or more parameters in order to deduce meaningful information.
[0022] Although the present disclosure is explained considering that the analytics framework 102 is implemented on a server, it may be understood that the analytics framework 102 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment. It will be understood that the analytics framework 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user 104 or stakeholders, hereinafter, or applications residing on the user devices 104. In one implementation, the analytics framework 102 may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications. 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 analytics framework 102 through a network 106.
[0023] 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.
[0024] Referring now to Figure 2, the analytics framework 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the analytics framework 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.
[0025] 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 analytics framework 102 to interact with the user directly or through the client devices 104. Further, the I/O interface 204 may enable the analytics framework 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.
[0026] The memory 206 may include any computer-readable medium or computer program product 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.
[0027] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. In one implementation, the modules 208 may include a data integration module 212, an analytics model repository creation module 214, a parameter configuration module 216, an data analytics module 218, an information displaying module 220, and other modules 222. The other modules 222 may include programs or coded instructions that supplement applications and functions of the analytics framework 102. The modules 208 described herein may be implemented as software modules that may be executed in the cloud-based computing environment of the analytics framework 102.
[0028] 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 central database 224, an analytics model repository 226, and other data 228. The other data 228 may include data generated as a result of the execution of one or more modules in the other modules 222.
[0029] As there are various challenges observed in the existing art, the challenges necessitate the need to build the analytics framework 102 for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, to deduce meaningful information. In order to deduce meaningful information, at first, a user may use the client device 104 to access the analytics framework 102 via the I/O interface 204. The user may register them using the I/O interface 204 in order to use the analytics framework 102. In one aspect, the user may access the I/O interface 204 of the analytics framework 102. The analytics framework 102 may employ the data integration module 212, the analytics model repository creation module 214, the parameter configuration module 216, the data analytics module 218, and the information displaying module 220. The detail functioning of the modules are described below with the help of figures.
[0030] It may be understood that each vehicle manufacturer has its respective vehicle platform database capable of storing data being received from a plurality of sensors and/or IoT devices deployed on the vehicle. Each respective vehicle platform database aggregates the data, in large volume, as each vehicle generates large amount of data while running. It may be understood that the data received may be stored in a native data format in the respective vehicle platform databases. Since there are numerous vehicle manufacturers thus the data being generated may be in distinct formats and hence may not be feasible for an analytics platform, connected with the plurality of vehicle platform databases, to perform analytics on the data being received in distinct formats and thus may not deduce meaningful information.
[0031] The analytics framework 102 facilitates to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information. It may be understood that the data may be captured in distinct formats from disparate connected vehicles and stored in respective vehicle platform databases. In one aspect, the data may comprise vehicle data and driver behavioral data. Once the data is stored, the data integration module 212 integrates each vehicle platform database, corresponding to a vehicle manufacturer, with the central database 224. It may be understood that the analytics framework 102 may implement standard interfaces that facilitate to interface with any vehicle platform database.
[0032] After integrating each vehicle platform database with the central database 224, the data integration module 212 populates the data stored in each vehicle platform database into the central database 224. It may be understood that the data may be populated in the central database 224 by a Push technology. Examples of the Push technology may include, but not limited to, Webpush, HTTPserver push, Pushlet, Long polling, and, Flash XMLSocket relays. In one aspect, the data populated in the central database 224 is one of historical data, real-time data, or both. In one aspect, the data may comprise a plurality of parameters including, but not limited to, comprises vehicle speed, GPS location, Rotation per Minute (RPM), Engine temperature, engine / brake pedal status, gear transmission, and fuel consumption. It may be understood that the plurality of parameters may include any set of vehicle parameters that are relevant for performing analytics on the data pertaining to the connected vehicle. Further, it may be understood that the plurality of parameters may be populated to the analytics framework over the generic and flexible interface(s) as defined by the framework.
[0033] Since the analytics framework 102 is language agnostics for example via restful Application Programming Interfaces (APIs) the analytics framework 102 may receive the plurality of parameters, in any format, from the vehicle platform database and thereby capable of process the plurality of parameters in order to deduce the meaningful information. Example of the APIs may include, but not limited to, HTTP based web services APIs. It may be further understood that a secure mechanism has been implemented to ensure secured communication of the data over implementation of the APIs. In another embodiment, the data may be populated upon transforming the native data format, of the data, into a format supported by the central database 224. It may be understood that the data may be transformed by using at least one transformation technique.
[0034] Further, the analytics model repository creation module 214 enables the user to create an analytics model repository 226 comprising a plurality of use-case models. It may be understood that each use-case model may be configured to perform a predefined analysis. It may be noted that any number of use-case model may be created for performing analysis as per the desired need of the user. The user may be at least one of an individual user, law enforcing agencies, government agencies, or insurance agencies. Some of the analysis performed by using use-case models may include, but not limited to, driving performance analysis, vehicle engine performance analysis, and fuel performance analysis.
[0035] In order to perform each of the aforementioned analysis, the parameter configuration module 216 configures one or more parameters, of the plurality of parameters, with each use-case model. In one aspect, the one or more parameters may be configured based on the meaningful information expected from each respective use-case model. In other words, each use-case model may not require each of the plurality of parameters. Therefore each use-case model may fetch the required parameters, as configured, from the data to perform analysis.
[0036] Subsequent to the configuration of the one or more parameters with each user-case, the data analytics module 218 performs analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models, in order to deduce meaningful information. In one aspect, the analytics may be performed on the one or more parameters by using a predefined formulation and/or equation. Once the analysis is performed, the information displaying module 220 displays the meaningful information on a User Interface (UI) such as dashboard. The dashboard may be configured with the analytics framework 102 to fetch the analytics outcome or meaningful information from the central database 224 and displayed it to the user. In one aspect, the meaningful information may be displayed in the form of charts and/or graphs, Histograms, or Scatter plots and any other intuitive mode of displaying the meaningful information to the user. In one embodiment, the meaningful information may be represented on the UI in a plurality of formats/layouts as prescribed by the user. It may be understood that the dashboard may be implemented on a web portal, a native application or mobile based application.
[0037] In order to elucidate the functioning of the modules 208, as aforementioned, consider an example where a plurality of vehicle manufacturers have their respective vehicle platform databases 502-1, 502-2, 502-3…502-n, as shown in figure 3. Each of the vehicle platform databases is connected with a plurality of vehicles manufactured by respective vehicle manufacturers. For example, Car C1, C2, and C3, manufactured by M1, are connected to the vehicle platform database 502-1. Similarly Car C3, C4, and C5, manufactured by M2, are connected to the vehicle platform database 502-2. Similarly, Car C7, C8, and C9, manufactured by M3, are connected to the vehicle platform database 502-3. It may be understood that Car C1, C2, and C3 …. C7, C8, and C9 are connected vehicles and deployed with a plurality of sensors and/or IoT devices capable of transmitting the data, comprising a plurality of parameters, to their respective vehicle platform databases i.e. 502-1, 502-2, or 502-3 via at least one mobile network operator.
[0038] Once the data has been transmitted to their respective vehicle platform databases 502-1, 502-2, or 502-3, the data integration module 212 integrates each vehicle platform database, corresponding to a vehicle manufacturer M1, M2, or M3, with the central database 224. After integration, the data integration module 212 populates the data stored in each vehicle platform database into the central database 224 by using APIs. Further the analytics model repository creation module 214 enables a user to create an analytics model repository 226 comprising a plurality of use-case models. As shown in the figure, the analytics model repository 226 comprises use-case model UcM1, UcM2, UcM3, and UcMn. Each of the use-case model may be configured to perform specific analysis such UcM1 may be configured to perform driving performance analysis. Similarly UcM2 may be configured to perform vehicle engine performance analysis, UcM3 may be configured to perform fuel performance analysis, and likewise.
[0039] It may be noted that each use-case model doesn’t require the plurality of parameters but may require a subset of the plurality of parameters. Therefore the parameter configuration module 216 configures one or more parameters or the subset of the plurality of parameters with each use-case model. Such as parameters like vehicle speed, braking sequence, gear shift acceleration, cornering, engine RPM, gear/speed ratio may be configured with UcM1. Based on such parameters, the data analytics module 218 performs analytics on such parameters by using existing solutions in the art in order to deduce meaningful information. In this example, the meaningful information indicates “whether the driver is a safe driver or an unsafe driver”. In one embodiment, if the driver is determined as the unsafe driver, the analytics framework 102 may also generate specific driving advises to driver to become the safe driver. After deducing the meaningful information, the meaningful information may be displayed by the information displaying module 220 on the dashboard.
[0040] Thus, based on the above, the analytics framework 102 may perform analytics on the data, captured from disparate connected vehicles, to deduce meaningful information. In one embodiment, the analytics framework 102 may further be integrated with various servers related with law enforcement agencies servers or insurance agencies which might keep a track of the driving behavior in order calculate the premium of the insurance to be paid by such driver. In an exemplary embodiment of the invention, the aforementioned description pertaining to the analytics framework 102 is specifically meant for connected vehicles.
[0041] Referring now to Figure 4, a method 400 for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information is shown, in accordance with an embodiment of the present subject matter. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0042] The order in which the method 400 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 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the subject matter 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 400 may be considered to be implemented as described in the analytics framework 102.
[0043] At block 402, each vehicle platform database, corresponding to a vehicle manufacturer, may be integrated with a central database 224. In one aspect, each vehicle platform database stores data in a native data format. In one aspect, the data may comprise a plurality of parameters. In one implementation, each vehicle platform database may be integrated by the data integration module 212.
[0044] At block 404, the data stored in each vehicle platform database may be populated into the central database 224. In one implementation, the data may be populated by the data integration module 212.
[0045] At block 406, a user may be enabled to create an analytics model repository comprising a plurality of use-case models. In one aspect, each use-case model may be configured to perform a predefined analysis. In one implementation, a user may be enabled to create an analytics model repository 226 by the analytics model repository creation module 214.
[0046] At block 408, one or more parameters, of the plurality of parameters, may be configured with each use-case model. In one aspect, the one or more parameters may be configured based on meaningful information expected from each respective use-case model. In one implementation, the one or more parameters may be configured with each use-case model by the parameter configuration module 216.
[0047] At block 410, analytics may be performed on the one or more parameters associated with a use-case model, of the plurality of use-case models. In one aspect, the analytics may be performed on the one or more parameters in order to deduce meaningful information. In one implementation, the analytics may be performed on the one or more parameters by the data analytics module 218.
[0048] 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.
[0049] Some embodiments enable a system and a method to provide various analytics use-case model that may be easily integrated with the system without affecting the existing framework.
[0050] Some embodiments enable a system and a method to provide various analytics by simply selecting the desired use-case from the analytics model repository.
[0051] Although implementations for methods and systems for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information 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. Rather, the specific features and methods are disclosed as examples of implementations for performing analytics on the data in order to deduce meaningful information.

Claims:1. A method for facilitating an analytics framework for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information, the method comprising:
integrating, by a processor (202), each vehicle platform database, corresponding to a vehicle manufacturer, with a central database (224), wherein each vehicle platform database stores data in a native data format, and wherein the data comprises a plurality of parameters;
populating, by the processor (202), the data stored in each vehicle platform database into the central database (224);
enabling, by the processor (202), a user to create an analytics model repository (226) comprising a plurality of use-case models, wherein each use-case model is configured to perform a predefined analysis;
configuring, by the processor (202), one or more parameters, of the plurality of parameters, with each use-case model, wherein the one or more parameters are configured based on meaningful information expected from each respective use-case model; and
performing, by the processor (202), analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models, wherein the analytics is performed on the one or more parameters in order to deduce meaningful information.

2. The method of claim 1, wherein the data is populated upon transforming the native data format, of the data, into a format supported by the central database, and wherein the data is transformed by using at least one transformation technique.

3. The method of claim 1, wherein the data comprise vehicle data and driver behavioral data, and wherein the plurality of parameters comprises vehicle speed, GPS location, Rotation per Minute (RPM), Engine temperature, engine / brake pedal status, gear transmission, and fuel consumption.

4. The method of claim 1, wherein the meaningful information is represented on a User Interface in a plurality of formats.

5. The method of claim 1, wherein the data populated in the central database is one of historical data, real-time data, or both.

6. An analytics framework (102) for connected vehicles to perform analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information, the analytics framework (102) comprising:
a processor (202); and
a memory (206) coupled to the processor (202), wherein the processor (202) is capable of executing a plurality of modules (208) stored in the memory (206), and wherein the plurality of modules (208) comprising:
a data integration module (212) for
integrating each vehicle platform database, corresponding to a vehicle manufacturer, with a central database (224), wherein each vehicle platform database stores data in a native data format, and wherein the data comprises a plurality of parameters, and
populating the data stored in each vehicle platform database into the central database (224);
an analytics model repository creation module (214) for enabling a user to create an analytics model repository (226) comprising a plurality of use-case models, wherein each use-case model is configured to perform a predefined analysis;
a parameter configuration module (216) for configuring one or more parameters, of the plurality of parameters, with each use-case model, wherein the one or more parameters are configured based on meaningful information expected from each respective use-case model; and
a data analytics module (218) for performing analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models, wherein the analytics is performed on the one or more parameters in order to deduce meaningful information.

7. The analytics framework of claim 6, wherein the data is populated upon transforming the native data format, of the data, into a format supported by the central database, and wherein the data is transformed by using at least one transformation technique.

8. The analytics framework of claim 6, wherein the data comprise vehicle data and driver behavioral data, and wherein the plurality of parameters comprises vehicle speed, GPS location, Rotation per Minute (RPM), Engine temperature, engine / brake pedal status, gear transmission, and fuel consumption.
9. The analytics framework of claim 6 further comprises an information displaying module (220) for representing the meaningful information on a User Interface (UI) in a plurality of formats.

10. A non-transitory computer readable medium embodying a program executable in a computing device for facilitating an analytics framework for connected vehicles to performing analytics on data, captured from disparate connected vehicles, in order to deduce meaningful information, the program comprising a program code:
a program code for integrating each vehicle platform database, corresponding to a vehicle manufacturer, with a central database, wherein each vehicle platform database stores data in a native data format, and wherein the data comprises a plurality of parameters;
a program code for populating the data stored in each vehicle platform database into the central database;
a program code for enabling a user to create an analytics model repository comprising a plurality of use-case models, wherein each use-case model is configured to perform a predefined analysis;
a program code for configuring one or more parameters, of the plurality of parameters, with each use-case model, wherein the one or more parameters are configured based on meaningful information expected from each respective use-case model; and
a program code for performing analytics on the one or more parameters associated with a use-case model, of the plurality of use-case models, wherein the analytics is performed on the one or more parameters in order to deduce meaningful information.