Patent Publication Number: US-9846747-B2

Title: System and method of data compression

Description:
PRIORITY CLAIM 
     This U.S. patent application claims the benefit of priority under 35 U.S.C. §119 to India patent application no. 68/MUM/2014, filed on Jan. 8, 2014. The aforementioned application is incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     The present subject matter described herein, in general, relates to data compressing, and more particularly to a system and method for adaptively compressing data based on compression parameters. 
     BACKGROUND 
     With an advent of various embedded systems and communication infrastructure, a cyber-physical system may be monitored in real-time based on data captured from one or more sensors. Based on the capturing of the data, it has been observed that the data may be analyzed on-the-fly or the data may be transmitted to a server for real-time monitoring or deriving statistical inference from the data. 
     For example, insurance companies may use a telematics application for real-time monitoring of a driving pattern of a driver driving a vehicle. The driving pattern may be monitored in the real-time based on transmission of the data, captured by the one or more sensors deployed on the vehicle, to the server that may be associated to the insurance companies. In one aspect, the one or more sensors (such as accelerometer sensor, tachometer) may facilitate capturing of the data associated to the vehicle in transit. The data captured may further be transmitted to the server over a telecommunication network for the real-time monitoring of the driving pattern of the driver by analyzing the data. 
     In order to analyze the data for deriving the statistical inferences, the telematics application may require a large set of the data. For example, when a vehicle undertakes long duration trips like cross-country travels, the large set of the data may be transmitted over the telecommunication network for analysis. The transmission of the large set of the data may lead to incur high costs. In addition, the transmission of the large set of the data may further require high transmission bandwidth of the telecommunication network and data storage for storing the large set of the data. 
     In view of the above, there exists a challenge to transmit the data within the constraints of the transmission bandwidth and the data storage. Further, there exists a challenge related to quality of the data that may be impacted after compressing the data using data compression techniques. Data compression may impact the quality of the data, thereby resulting in major inaccuracies while deriving the statistical inference based on the analysis performed on the data. 
     SUMMARY 
     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 disclosures. 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 aspects related to systems and methods for compressing a dataset of a plurality of datasets captured from one or more sensors and the aspects 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. 
     In one embodiment, a system for compressing a dataset of a plurality of datasets captured from one or more sensors is disclosed. The system may comprise a hardware processor; and a memory coupled to the processor, wherein the processor is capable of executing instructions stored in the memory to perform a method. The method may comprise filtering, via one or more hardware processors, a dataset from a plurality of datasets based on occurrence of an event. Further, the method may comprise determining, via the one or more hardware processors, a quality of information index for the dataset, wherein the quality of information index indicates a measure of quality of the dataset, wherein the quality of information index is determined based on a quality of information estimation function. Also, the method may comprise comparing, via the one or more hardware processors, the quality of information index with a list of quality of information indices stored in a lookup table in order to identify a target quality of information index and compression parameters corresponding to the quality of information index, wherein the target quality of information index is indicative of a reference measure of quality of the dataset, and wherein the reference measure is applicable for deriving statistical inferences based on analysis performed on the dataset. Further, the method may comprise inputting, via the one or more hardware processors, the compression parameters in a compression algorithm for compressing the dataset in order to achieve the target quality of information index for the analysis. 
     In one embodiment, a method for compressing a dataset of a plurality of datasets captured from one or more sensors is disclosed. The method may comprise filtering, via one or more hardware processors, a dataset from a plurality of datasets based on occurrence of an event. Further, the method may comprise determining, via the one or more hardware processors, a quality of information index for the dataset, wherein the quality of information index indicates a measure of quality of the dataset, wherein the quality of information index is determined based on a quality of information estimation function. Also, the method may comprise comparing, via the one or more hardware processors, the quality of information index with a list of quality of information indices stored in a lookup table in order to identify a target quality of information index and compression parameters corresponding to the quality of information index, wherein the target quality of information index is indicative of a reference measure of quality of the dataset, and wherein the reference measure is applicable for deriving statistical inferences based on analysis performed on the dataset. Further, the method may comprise inputting, via the one or more hardware processors, the compression parameters in a compression algorithm for compressing the dataset in order to achieve the target quality of information index for the analysis. 
     In one embodiment, a non-transitory computer program product is disclosed, having embodied thereon computer-executable instructions for compressing a dataset of a plurality of datasets captured from one or more sensors. The instructions may comprise instructions for performing a method. The method may comprise filtering, via one or more hardware processors, a dataset from a plurality of datasets based on occurrence of an event. Further, the method may comprise determining, via the one or more hardware processors, a quality of information index for the dataset, wherein the quality of information index indicates a measure of quality of the dataset, wherein the quality of information index is determined based on a quality of information estimation function. Also, the method may comprise comparing, via the one or more hardware processors, the quality of information index with a list of quality of information indices stored in a lookup table in order to identify a target quality of information index and compression parameters corresponding to the quality of information index, wherein the target quality of information index is indicative of a reference measure of quality of the dataset, and wherein the reference measure is applicable for deriving statistical inferences based on analysis performed on the dataset. Further, the method may comprise inputting, via the one or more hardware processors, the compression parameters in a compression algorithm for compressing the dataset in order to achieve the target quality of information index for the analysis. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, there is shown in the present document example constructions of the disclosure; however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and the drawings. 
       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. 
         FIG. 1  illustrates a network implementation of a system for compressing a dataset of a plurality of datasets captured from one or more sensors is shown, in accordance with an embodiment of the present subject matter. 
         FIG. 2  illustrates the system, in accordance with an embodiment of the present subject matter. 
         FIG. 3  illustrates the components of the system in accordance with an embodiment of the present subject matter. 
         FIG. 4  illustrates a method for compressing the dataset of the plurality of datasets captured from the one or more sensors, in accordance with an embodiment of the present subject matter. 
     
    
    
     DETAILED DESCRIPTION 
     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. 
     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. 
     The present subject matter provides systems and methods for compressing a dataset of a plurality of datasets captured from one or more sensors are described. The one or more sensors, associated to a telematics application, may generate the plurality of datasets. The systems and the methods may detect an event to filter the dataset from the plurality of datasets based on detection of a pre-defined threshold value stored in a database, wherein the pre-defined threshold value may be associated to the event. In one aspect, the dataset may be filtered in order to derive statistical inferences by performing analysis on the dataset. In an embodiment, the dataset may be utilized by insurance companies in order to derive insurance premiums for various insurance products. 
     For example, the insurance companies may utilize the dataset to derive an insurance premium applicable for a driver, driving a vehicle, by using the telematics application. In order to derive the insurance premium, the telematics application may capture the plurality of datasets from the one or more sensors such as an accelerometer or a tachometer deployed on the vehicle. It may be understood that the insurance premium may be determined based on a risk score associated to the driver. The risk score may be determined based on occurrence of one or more events such as hard cornering, hard acceleration or hard deceleration of the vehicle driven by the driver. It may be understood that, the occurrence of the one or more events may be determined based on the detection of the pre-defined threshold value associated to the one or more events. On occurrence of the one or more events (the hard cornering, the hard acceleration or the hard deceleration), the dataset may be filtered from the plurality of datasets. Subsequent to the filtration, the dataset may be compressed in accordance with a target quality of information (QoI) index, such that the compression of the dataset may not impact on the quality of the dataset while deriving the statistical inferences. The target QoI may indicate a reference measure of quality of the dataset required for deriving the statistical inferences. 
     In order to compress the dataset in accordance with the target QoI, a quality of information (QoI) index for the dataset may be determined using a QoI estimation function. The QoI index may indicate a measure of quality of the dataset. In one aspect, after determining the QoI index, the QoI index may be compared with a list of QoI indices stored in a lookup table. In one aspect, the QoI index may be compared with the list of QoI indices in order to identify the target QoI index and compression parameters corresponding to the QoI index for the dataset. In one aspect, the lookup table may contain the list of QoI indices mapped with a list of target QoI indices and a list of compression parameters. 
     It may be understood that, the statistical inferences may be derived by performing the analysis on the dataset. The dataset may be compressed by using the compression parameter as identified. In one embodiment, the target QoI index may be identified from the list of target QoI indices stored in the lookup table. The compression parameters, on the other hand, may be identified from the list of compression parameters stored in the lookup table. 
     Subsequent to the identification of the target QoI index and the compression parameters, the compression parameters may be inputted into a compression algorithm. The compression parameters may facilitate in achieving the target QoI index on compressing the dataset using the compression algorithm. Examples of the compression algorithm may include, but not be limited to, lossless data compression, and a lossy compression. In this manner, based on the target QoI, the statistical inferences may be derived by performing the analysis on the dataset. Subsequent to the compression of the dataset, the dataset may then be transmitted to a centralized server associated to the insurance companies in order to derive the statistical inferences such as the insurance premium applicable for the driver driving the vehicle. 
     While aspects of described system and method for compressing a dataset of a plurality of datasets captured from one or more sensors 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. 
     Referring now to  FIG. 1 , a network implementation  100  of a system  102  for compressing a dataset of a plurality of datasets captured from one or more sensors is illustrated, in accordance with an embodiment of the present subject matter. In one embodiment, the system  102  may filter the dataset from the plurality of datasets based on an occurrence of an event. Upon filtering the dataset, the system  102  may determine a quality of information (QoI) index for the dataset, wherein the QoI index may indicate a measure of quality of the dataset. Subsequent to the determination of the QoI index, the system  102  may compare the QoI index with a list of QoI indices stored in a lookup table. In one aspect, the system  102  may compare the QoI index with the list of QoI indices for identifying a target QoI index and compression parameters corresponding to the QoI index. Based on the identification, the system  102  may input the compression parameters in a compression algorithm for compressing the dataset in order to achieve the target QoI index for the analysis. 
     Although the present subject matter is explained considering that the system  102  is implemented on 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 multiple users through one or more user devices  104 - 1 ,  104 - 2  . . .  104 -N, collectively referred to as user devices  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 . 
     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 may represent an association of the different types of networks that can 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. 
     Referring now to  FIG. 2 , the system  102  is illustrated in accordance with an embodiment of the present subject matter. 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  may be configured to fetch and execute computer-readable instructions stored in the memory  206 . 
     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 client devices  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. 
     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 . 
     The modules  208  may include routines, programs, objects, components, data structures, etc., which can perform particular tasks, functions or implement particular abstract data types. In one implementation, the modules  208  may include a data filtering module  212 , a determination module  214 , a comparison module  216 , a data compression module  218 , and other modules  220 . The other modules  220  may include programs or coded instructions that supplement applications and functions of the system  102 . 
     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  222 , and other data  224 . The other data  224  may include data generated as a result of the execution of one or more modules in the other modules  220 . 
     In one implementation, at first, a user may use the client device  104  to access the system  102  via the I/O interface  204 . Users may register themselves using the I/O interface  204  in order to use the system  102 . The working of the system  102  may be explained in detail in  FIGS. 3 and 4  explained below. The system  102  may be used for compressing a dataset of a plurality of datasets captured from one or more sensors. In order to compress the dataset, the system  102 , at first, filters the dataset from the plurality of sensors that may generate the plurality of datasets, wherein the one or more sensors that may be deployed on a vehicle. It may be understood that the dataset may filtered by the data filtering module  212  based on occurrence of an event. 
     Referring to  FIG. 3 , a detailed working of the components of the system  102  is illustrated, in accordance with an embodiment of the present subject matter. The system  102  may comprise a data filtering module  212 , a determination module  214 , a comparison module  216 , and a data compression module  218 . In one implementation, in order to filter the dataset from the plurality of datasets generated by the one or more sensors ( 302 - 1 ,  302 - 2 ,  302 - 2 ), hereinafter referred to as sensors  302 , the data filtering module  212  may filter the dataset from the plurality of datasets based on occurrence of one or more event hereinafter referred to as event  304 . The occurrence of the event  304  may be determined based on a detection of pre-defined threshold value stored in a database  222 , wherein the pre-defined threshold value may be associated to the event  304 . On occurrence of the event  304 , the dataset may be filtered from the plurality of datasets in order to derive statistical inferences by performing analysis on the dataset. 
     In order to understand working of the filtering module  212  as described above, consider an example where the dataset may be filtered from the plurality of the datasets. The dataset may be utilized by insurance companies for computing an insurance premium applicable for the driver driving the vehicle. In one aspect, the insurance premium may be computed by using a telematics application. It may be understood that, for computing the insurance premium for the driver driving the vehicle using the telematics application, the sensors  304  (such as an accelerometer or a tachometer) may be deployed on the vehicle. As the driver is driving the vehicle, the plurality of datasets may be generated by the sensors  304 , wherein the plurality of datasets indicates driving pattern of the driver. Subsequent to the generation of the plurality of datasets, the data filtering module  212  may filter the dataset from the plurality of datasets based on occurrence of the event  304 , wherein the event  304  may be occurred on the detection of the pre-defined threshold value corresponding to the event  304 . Examples of the event  304  corresponding to the telematics application for computing the insurance premium may include, but not limited to, a hard cornering, a hard acceleration or a hard deceleration. 
     In one embodiment, the event  304  may facilitate to determine a risk score associated to the driver of the vehicle. The risk score may be used by insurance companies to compute the insurance premium. In order to evaluate the risk score associated with a particular driving pattern of the driver, 3-axis acceleration may be continuously measured at 20 Hz sampling rate by using the sensors  302  such as the accelerometer. It may be understood that, the pre-defined threshold value, corresponding to the event  304  (i.e. the hard cornering, the hard acceleration or the hard deceleration), may be pre-defined in the database  222  for determining the risk score. In one aspect, the event  304  may be detected by applying the pre-defined threshold value on acceleration values, wherein the acceleration values may be determined by using the sensors  302  deployed on the vehicle. In one embodiment, the occurrence of the event  304  may be defined as follows: 
     In one example, the hard cornering may be related to lateral dynamics of the vehicle. It may be understood that, in terms of overturning limit for the vehicle in transit, the vehicle with mass ‘M’ is cornering with a lateral acceleration of ‘a x  (m/sec2)’. Based on the lateral acceleration, maximum lateral acceleration is given by
 
α x   max   =μ·g   (1)
 
     where, μ is the coefficient of friction. In one aspect, if ‘μ’=1, then the maximum lateral acceleration that may trigger the overturn the vehicle is:
 
α x   max   =g   (2)
 
     It may be understood that, the “static stability factor” is greater than ‘1’. Thus, based on the above observation from the above equations (1) and (2), the overturn may be determined by coefficient of friction between road and the tires of the vehicle. In one aspect, the lateral acceleration values may be normalized with respect to ‘g’. Such that, 
     
       
         
           
             
               
                 
                   
                     Measured 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     Cornering 
                     ⁢ 
                     
                       : 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     M 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     C 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     T 
                   
                   = 
                   
                     
                       ± 
                       
                         
                           measured 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           lateral 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           acceleration 
                         
                         g 
                       
                     
                     ≤ 
                     1.0 
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     Based on the equations (3), the pre-defined threshold value for the hard cornering (MCT) values greater than 0.5 are recorded as the event  304 . 
     In another example, the hard acceleration or the hard deceleration may be related to longitudinal dynamics of the vehicle. In one aspect, the limits for achieving longitudinal acceleration given by friction limits as well as the hazard of tilting may be determined by:
 
α y   max   =∓μ·g   (4)
 
     In one aspect, consider ‘μ’=1, the limit for the longitudinal acceleration is given as 
     
       
         
           
             
               
                 
                   
                     Measured 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     acceleration 
                     ⁢ 
                     
                       : 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     M 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     A 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     T 
                   
                   = 
                   
                     ± 
                     
                       
                         Measured 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         longitudal 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           acceleration 
                           / 
                           deceleration 
                         
                       
                       g 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     with maximum value as ‘1’. In one embodiment, the pre-defined threshold value for the MCT and the MAT is ‘0.5 g’. It may be understood that, whenever, the acceleration, determined from the sensors  302 , is greater than the ‘0.5 g’, such dataset may be considered as relevant and thereby filtered from the plurality of datasets by the filtering module  212 . The dataset may be then transmitted to a centralized server of the insurance companies in order to derive the statistical inferences, based on the risk score, such as the insurance premium applicable for the driver driving the vehicle. 
     It may be understood that, the dataset transmitted to the centralized server should be compressed in accordance with a target quality of information (QoI) index, such that the compression of the dataset may not impact on the quality of the dataset while deriving the statistical inferences. In one embodiment, in order to compress the dataset in accordance with the target QoI, the determination module  214  may determine a quality of information (QoI) index for the dataset. The QoI index may indicate a measure of quality of the dataset. It may be understood that, the QoI index may be determined based on a QoI estimation function. In one embodiment, the QoI estimation function may be defined as follows: 
     
       
         
           
             
               
                 
                   QoI 
                   = 
                   
                     
                       ∑ 
                       i 
                     
                     ⁢ 
                     
                       
                         w 
                         i 
                       
                       ⁢ 
                       
                         
                           f 
                           i 
                         
                         ⁡ 
                         
                           [ 
                           
                             a 
                             ⁡ 
                             
                               ( 
                               z 
                               ) 
                             
                           
                           ] 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     where the ‘i’ indicates the dataset of the plurality of datasets for which the QoI is to be determined, ‘w i ’ indicates weight that are chosen to normalize the QoI and ‘f i [a(z)]’ may be computed to derive an indicator of the rate of change of the measured acceleration of the vehicle. Now in order to understand the determination of the QoI using the equation (6), consider an application where long-term reliability of suspension is to be measured. In one aspect, the long-term reliability of the suspension may be measured by computing QoI ‘jerk energy’  on the vehicle from the measured vertical acceleration ‘f i  [a(z)]’. 
     In one embodiment, 
     
       
         
           
             
               
                 
                   QoI 
                   = 
                   
                     
                       ∑ 
                       i 
                     
                     ⁢ 
                     
                       
                         w 
                         i 
                       
                       ⁢ 
                       
                         
                           f 
                           i 
                         
                         ⁡ 
                         
                           [ 
                           
                             a 
                             ⁡ 
                             
                               ( 
                               z 
                               ) 
                             
                           
                           ] 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     where f i [a(z)]=J si   2 , wherein ‘J si   2 ’ indicates ‘jerk energy’. In one aspect, the Jerk energy may be defined as below: 
     Consider, ‘a 1 ’, ‘a 2 ’, . . . , ‘a n ’ be the measured acceleration samples of the vehicle at time ‘t 1 ’, ‘t 2 ’, . . . , ‘t n ’ where Δt=t n −t n-1  for uniform sampling rate. 
     Based on the above description, the ‘jerk energy’ may be defined as 
     
       
         
           
             
               
                 
                   
                     J 
                     si 
                   
                   = 
                   
                     
                       
                         
                           a 
                           
                             i 
                             + 
                             1 
                           
                         
                         - 
                         
                           a 
                           i 
                         
                       
                       
                         Δ 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         t 
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     for 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ( 
                       
                         1 
                         ≤ 
                         i 
                         ≤ 
                         
                           n 
                           - 
                           1 
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     where ‘n’ indicates total number of the measured acceleration samples. Based on the equation (7), the ‘QoI’ for the total number of the measured acceleration samples may be determined by:
 
QoI= w   1   J   s1   2   +w   2   J   s2   2   + . . . +w   19   J   s19   2   (9)
 
     In one aspect, the weight (w 1 , w 2 , . . . , w 19 ) associated to each dataset, as illustrated in equation 9, may be normalized by performing at least one arithmetic/mathematical operation in such a manner that the QoI is within the range of 0 to 1. It may be understood that, ‘s’ indicates the time-window of ‘1 sec’ (therefore 20 samples of the measured acceleration is captured). In one aspect, if the ‘jerk energy’ J s1   2 =1500 m 2 /s 6  as extreme value, towards damage to the suspension of the vehicle, then the ‘jerk energy’ sequence {J s1   2 , J s2   2 , . . . , J s19   2 } may be normalized with respect to 1500 m 2 /s 6 , the QoI jerk energy  may be determined by using the equation (7). Similarly QoI hard acceleration  index, QoI hard acceleration  index and QoI hard cornering  index may also be determined by using the equation (7). 
     Based on the determination of the QoI index, the comparison module  216  may compare the QoI index with a list of QoI indices stored in a lookup table. The lookup table may contain the list of QoI indices mapped with a list of target QoI indices and a list of compression parameters. In order to identify the target QoI index and compression parameters for the dataset, the comparison module  216  may compare the QoI index with the list of QoI indices stored in the database  222 . The target QoI index may indicate a reference measure of quality of the dataset for deriving the statistical inferences based on analysis performed on the dataset compressed by using the compression parameters. In one embodiment, the target QoI index may be identified from the list of target QoI indices. The compression parameters, on the other hand, may be identified from the list of compression parameters. It may be understood that, the list of target QoI indices and the compression parameters are stored in the look-up table located in the database  222 . 
     Based on the identification of the target QoI index and the compression parameters corresponding to the dataset, the data compression module  218  may input the compression parameters in a compression algorithm. Examples of the compression parameters may include wavelet transform, a threshold on number of transform coefficients and quantizer decision boundaries. Examples of the compression algorithm may include lossy compression based on Huffman encoding of wavelet transform coefficients computed for the dataset. In one embodiment, in order to compress the dataset, the lossy compression may comprise computation of the transform coefficients of the dataset by using at least one transformation technique. Examples of the at least one transformation technique may include the wavelet transform, a discrete cosine transform, or Fourier transform. Subsequent to the computation of the transform coefficients, the threshold on the number of the transform coefficients and quantization of the transform coefficients may be performed using conventional quantizers in order to minimize the distortion. Examples of the conventional quantizers may include, but not be limited to, scalar quantizer or vector quantizer. After quantizing the transform coefficients, the transform coefficients may then be encoded by using lossless encoding techniques. Examples of the lossless encoding techniques may include Huffman encoding or arithmetic coding. 
     In one embodiment, the compression parameters may be used to compress the dataset when the QoI index is less than the target QoI index. It may be understood that, the compression parameters may compress the dataset based on following factors: 
     When the QoI hard acceleration index &gt;target hard acceleration index  the QoI hard acceleration  index&gt;target hard deceleration  index and the QoI hard cornering  index&gt;target hard cornering  index, then the compression parameters, i.e., the scale of wavelet transform, the threshold value for selecting transform coefficients and the quantization size, may be identified from the lookup table corresponding to the QoI hard acceleration  index, the QoI hard acceleration  index and the QoI hard cornering  index respectively. On the other hand, when the QoI hard acceleration  index&lt;the target hard acceleration  index, the QoI hard acceleration  index&lt;the target hard deceleration  index and the QoI hard cornering  index&lt;the target hard cornering  index, then the dataset may either not compressed or may be compressed by using default compression parameters. The default compression parameters may indicate that the dataset may be compressed by using pre-defined compression parameters. 
     In one embodiment, upon compressing the dataset in accordance with the target QoI index, the dataset may then be transmitted to the centralized server, such that the compression of the dataset may not impact on the quality of the dataset while deriving the statistical inferences. 
     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. 
     Some embodiments can enable a system and a method for filtering a dataset from a plurality of datasets based on occurrence of an event in order to optimize storage capacity of a data storage medium that may have limited capacity. 
     Some embodiments can enable the system and the method for measuring of quality of information (QoI) as applicable on the dataset for end objective and tying up such measure to re-configurability of the data compression algorithm. 
     Referring now to  FIG. 4 , a method  400  for compressing a dataset of a plurality of datasets captured from one or more sensors 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. 
     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 in the above described system  102 . 
     At block  402 , the dataset may be filtered from the plurality of datasets on occurrence of an event. In one implementation, the dataset may be filtered by the data filtering module  212 . 
     At block  404 , a quality of information (QoI) index for the dataset may be determined based on a QoI estimation function. In one aspect, the QoI index may indicate a measure of quality of the dataset. In one implementation, the QoI index may be determined by the determination module  214 . 
     At block  406 , the QoI index may be compared with a list of QoI indices stored in a lookup table. In one aspect, the QoI index may be compared with a list of QoI indices for identifying a target QoI index and compression parameters corresponding to the QoI index. The target QoI index may be indicative of a reference measure of quality of the dataset applicable for deriving statistical inferences based on analysis performed on the dataset. In one implementation, the target QoI index and the compression parameters may be identified by the comparison module  216 . 
     At block  408 , the compression parameters may be inputted in a compression algorithm. In one aspect, the compression parameters may be inputted for compressing the dataset in order to achieve the target QoI index for the analysis. In one implementation, the compression parameters may be inputted by the data compression module  218 . 
     Although implementations for methods and systems for compressing the dataset of the plurality of datasets captured from the one or more sensors 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 compressing the dataset of the plurality of datasets captured from the one or more sensors.