FORM2
THEPATENTS ACT, 1970
(39 of 1970)
&
THEPATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
1. Title of the invention: METHOD AND SYSTEM FOR CONDUCTING A SURVEY
2. Applicant(s)
NAME NATIONALITY ADDRESS
TATA CONSULTANCY
SERVICES LIMITED
Indian Nirmal Building, 9th Floor, Nariman
Point, Mumbai, Maharashtra 400021,
India
3. Preamble to the description
COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it
is to be performed.
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TECHNICAL FIELD
[0001] The present subject matter relates to methods and systems for
conducting a survey of one or more survey items for multiple data stores.
BACKGROUND
[0002] A survey is often conducted for a plurality of retail stores in a
geographical area that offer a variety of products to consumers. Retail stores are
usually located over a wide area, including urban, semi-urban and rural areas, in
order to offer products wide spread. The products may include household items,
food items, electronics goods, and such. The survey may be conducted to gather
the retail or sales data for the retail stores for various purposes. The gathered data
may be for the retail of products on a daily-basis, fortnight-basis, monthly-basis,
or the like, and, based on the gathered retail data, market research firms may
generate analytics to grasp a tendency of consumers’ choices, product popularity,
etc.
[0003] A similar survey may also be conducted for other centers or places,
for example hospitals, farming fields, and such, in a geographical area depending
on the requirements. The survey may be conducted for a plurality of hospitals in
geographical area to gather the number of cases reported for a disease endemic in
that area or the number of cured cases for a disease in that area. Such a survey
may be conducted for hospitals for various purposes, including analytics of the
spread of the disease and how efficiently it was cured. Similarly, the survey may
be conducted for a plurality of farming fields in a geographical area to gather
yield details of a crop at those farming fields. Such a survey may be conducted for
the farming fields for various purposes, including analytics of average yield of the
crop in the area.
[0004] In order to conduct surveys for centers or places, like the retail
stores, the hospitals or the farming fields, substantially high investments towards
resources, management, and infrastructure, are required. Thus, it is important to
identify methodologies for conducting a survey which are substantially efficient
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and facilitate in reducing the investments towards resources, management, and
infrastructure required for the survey.
SUMMARY
[0005] This summary is provided to introduce concepts related to
conducting a survey of one or more products for multiple data stores. This
summary is neither 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.
[0006] In accordance with an embodiment of the present subject matter, a
method for conducting a survey of at least one survey item is described. The
method includes obtaining historical survey data of the at least one survey item
for T number of data stores. The method also includes determining a sparsity
number K associated with the historical survey data of the at least one survey item
for the T number of data stores and determining a target number M based on the
sparsity number K. The target number M is indicative of a reduced number of data
stores, present amongst the T number of data stores, for collection of current
survey data to estimate current survey data of the at least one survey item for the
T number of data stores.
BRIEF DESCRIPTION OF DRAWINGS
[0007] 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 figures to reference like features and
components. Some embodiments of system and/or methods in accordance with
embodiments of the present subject matter are now described, by way of example
only, and with reference to the accompanying figures, in which:
[0008] Figure 1 illustrates a method for conducting a survey of at least one
survey item, according to an embodiment of the present subject matter.
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[0009] Figure 2 illustrates a network environment implementing a survey
conducting system, according to an embodiment of the present subject matter.
[0010] It should be appreciated by those skilled in the art that any block
diagrams herein represent conceptual views of illustrative systems embodying the
principles of the present subject matter. Similarly, it will be appreciated that any
flow charts, flow diagrams, state transition diagrams, pseudo code, and the like
represent various processes which may be substantially represented in computer
readable medium and so executed by a computer or processor, whether or not such
computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0011] The present subject matter relates to systems and methods for
conducting a survey of one or more survey items for multiple data stores. The data
stores may be located at locations in a wide geographical area. Depending on the
survey, the data stores may include retail stores, such as shops and outlets; include
health centers, such as hospitals, clinics and dispensaries; and include agricultural
regions, such as farming fields. The survey item and the survey data are dependent
on the type of data stores for survey. In an example where the retail stores are the
data stores for the survey, the corresponding survey item may include retail
products, such as household items, food items, electronics goods, and the like, that
are offered by the retail stores to consumers, and the corresponding survey data
may include retail or sales data of the retail product. In an example where the
hospitals are the data stores for the survey, the corresponding survey item may
include a disease for which patients have visited the hospitals, and the
corresponding survey data may include number of case reported for the disease.
Further, in an example where the farming fields are the data stores for the survey,
the corresponding survey item may include a crop which is grown in the farming
fields, and the corresponding survey data may include yield of the crop.
[0012] Data stores included in the ambit of survey, for which survey data
of survey items are to be gathered may be substantially large in number. Also, the
types of survey items for which data are to be gathered maybe large in number.
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Conventional methods for conducting a survey rely of gathering of survey data of
all the survey items and for all the data stores. The survey data for all the data
stores and for all the survey items are typically gathered for the purpose of
generation of a substantially correct and true analytics from the survey data. With
the large number of data stores and, in addition, the large number of survey items,
the gathering of survey data for all the data stores and for all the survey items
typically requires substantially high investment towards resources, management,
infrastructure, etc.
[0013] In an example, for conducting a survey for data stores, survey data
may be gathered via a network, such as internet. For this, the survey data for the
data stores may be collected by sensors, such as billing terminals or machines, or
data-logging machines, and automatically sent to a central server for further
processing, like report generation. With the large number of data stores and the
large number of survey items for which the survey data is to be collected,
conventionally, the data collection for the survey consumes enormous bandwidth
of the network. For this, the internet connectivity needs to be substantially good,
for example, with a high bandwidth. With no internet connectivity or low
bandwidth the survey data may be not be effectively collected for all the data
stores and for all the survey items. The data collection may also get delayed due to
low bandwidth over the network.
[0014] Further, the data stores can be located in rural areas, urban areas
and semi-urban areas. In rural and semi-urban areas, where network connectivity
and/or internet penetration are substantially low, human surveyor(s) needs to
manually visit each and every data store to collect the survey data. With the large
number of data stores and the large number of survey items for which the survey
data is to be collected, conventionally, the data collection by human surveyors
requires substantially large manpower and coordination. Also, the data collection
for all the data stores and for all the survey items is time consuming.
[0015] The present subject matter describes systems and methods for
conducting a survey of one or more survey items for multiple data stores. The
methodology of the present subject matter involves estimation of survey data for
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one or more survey items for a large number of data stores based on survey data
collected or gathered for a fewer number of data stores. Said methodology relies
on reconstruction of survey data for all the data stores, in the ambit of survey,
from the survey data collected for the fewer data stores in accordance with the
present subject matter.
[0016] In accordance with the methodology of the present subject matter
for conducting a survey of a survey item from multiple data stores, historical
survey data of the survey item is obtained for T number of data stores. The
historical survey data may be understood as the past survey data, for example, a
month or more prior to conducting the current survey, for that survey item. The T
number of data stores are the total number of data stores under consideration or in
the ambit of survey. Based on the historical survey data, a sparsity number K is
determined which is indicative of the sparseness of the historical survey data of
the product for the T number of data stores in a mathematical domain. For
determining the sparsity number K, the obtained historical survey data may be
transformed from one domain to another using a predefined mathematical
transformation. The sparsity number K corresponds to the number of values of the
historical survey data that are not zero after the mathematical transformation.
Based on the sparsity number K, a target number M is determined which is
indicative of a reduced number of data stores, from or present amongst the T
number of data stores, for collection of survey data.
[0017] In an implementation, the target number M is a multiplication
factor s times the sparsity number K, such that the target number M is in a range
from about 0.3 times the T number to about 0.5 times the T number. In an
implementation, the multiplication factor s may be 1.5.
[0018] The target number M is a basis of determination of number of data
stores for which current survey data is to be collected. With this, the current
survey data of the survey item for the T number of data stores can be estimated in
accordance with the present subject matter. As the target number M is
substantially less, i.e., half or less than the T number, with respect to scenario
where a plurality of the data stores is in rural areas, the manpower requirement is
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substantially reduced as a smaller subset (M number) of the data stores may
actually be visited by the human surveyors. This allows the data collection to be
conducted with much higher frequency and also reduces the efforts and the cost of
data collection. With respect to the scenario where a plurality of data stores is in
urban areas, the amount of current survey data to be collected and transmitted
over a network is less, which reduces the bandwidth requirements.
[0019] Further, for conducting a survey of the survey item for the T
number of data stores, in an implementation, M number of data stores are
identified from the T number of data stores for the collection of the survey data.
The M number of data stores are randomly identified from the T number of data
stores. Based on the identification of the M number of data stores, current survey
data of the survey item for the M number of data stores are obtained. The current
survey data may be understood as the recent data, for example, of a day, of a
week, of a fortnight or of a month prior to the day the survey of the survey item is
conducted. Further, based on the current survey data for the M data stores, current
survey data of the survey item for the T number of data stores are estimated. In an
implementation, the current survey data of the survey item for the T number of
data stores are estimated based on an L1-minimization computation on the current
survey data obtained for the M number of data stores. The L1-minimizaton
computation allows in reconstruction of current survey data for the T number of
data stores from the current survey data for the lesser number, i.e., M number, of
data stores.
[0020] The methodology of the present subject matter is described above
for conducting a survey of one survey item for the T number of data stores. In an
implementation, the same procedure can be repeatedly carried out for more than
one survey item.
[0021] In another implementation, in case a survey is conducted of
multiple survey items based on the present subject matter, M number of data
stores are identified, for example, randomly, from the T data stores, and historical
survey data of the multiple survey items for M number of data stores are obtained.
After obtaining the historical survey data for the multiple survey items for the M
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number of data stores, M' number of data stores are selected from the M number
of data stores based on a correlation between the historical survey data of one or
more pairs of the survey items. Here, number M' is integer less than the number
M. The correlation may be based on the influence of data of one survey item on
the data of the other survey item in each pair. For example, the correlation may be
based on the influence of sales of one retail product on the sales of other retail
product. This correlation between certain pairs of survey items facilitates in
exploiting inter-item redundancy to reduce the number of data stores from which
current survey data is to be collected. Based on the selection of the M' number of
data stores, current survey data of the multiple survey items for the M' number of
data stores are obtained. Further, based on the current survey data for the M'
number of data stores, current survey data of the multiple survey items for the T
number of data stores are estimated. In an implementation, the current survey data
of each of the multiple survey items for the T number of data stores are estimated
based on L1-minimization computation on the current survey data obtained of the
corresponding survey item for the M' number of data stores.
[0022] With the collection of current survey data from a fewer number of
data stores in reference to the total number of data stores under consideration for
survey, the process of data gathering becomes substantially efficient and the
efforts required for the data gathering are substantially reduced. In addition, the
requirements of resources, management and infrastructure, and the time consumed
for data collection, are also substantially reduced in comparison to the
conventional methodologies. In an example, with the methodology of the present
subject matter, if the current survey data is gathered or collected over a network
the consumption of bandwidth of the network for the data collection is
substantially less. Similarly, with the methodology of the present subject matter, if
the current survey data is gathered or collected manually by human surveyors the
requirement of manpower to be deployed for the data collection is substantially
less.
[0023] These and other advantages of the present subject matter would be
described in greater detail in conjunction with the following figures. It should be
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noted that the description and figures merely illustrate the principles of the present
subject matter.
[0024] In an implementation, the methodology of the present subject
matter may be followed for conducting a survey for data stores such as retail
stores, hospitals, and farming fields. The corresponding survey item, as mentioned
earlier, may include a retail product offered for retail by the retail stores, a disease
for which patients have visited the hospitals, a crop which is grown in the farming
fields, and the corresponding survey data, as mentioned earlier, may include retail
or sales data of the retail product, number of case reported for the disease, and
yield of the crop. Although, the examples of retail stores, hospitals, and farming
fields as the data stores are provided in the specification herein, the methodology
of the present subject matter may be applied for conducting a survey of one or
more survey items for other centers, and places, albeit a few variations as
appreciated by a person skilled in the art.
[0025] Figure 1 illustrates a method 100 for conducting a survey of at least
one survey item. The method 100 may be implemented in a survey conducting
system which is described later in the description with reference to Figure 2.
[0026] The method 100 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, and functions that perform particular functions or implement particular
abstract data types. The method 100 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.
[0027] The order in which the method 100 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, or an alternative method.
Additionally, individual blocks may be deleted from the method without departing
from the spirit and scope of the subject matter described herein. Furthermore, the
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method 100 can be implemented in any suitable hardware, software, firmware, or
combination thereof.
[0028] In an implementation, we assume that the ambit of survey includes
T number (e.g. 10000 or more) of data stores in a geographical region. The survey
may be conducted of a single survey item or of multiple survey items for the T
number of data stores.
[0029] At block 102, historical survey data of the at least one survey item
for the T number of data stores are obtained. The historical survey data of the at
least one survey item may be pre-recoded in data books of the data stores, or prestored
in a storage device of the data stores or in a central database. The historical
survey data may be obtained from at least one of the data books, the storage
device, and the central database.
[0030] At block 104, a sparsity number K is determined based on the
historical survey data of each of the at least one survey item for the T number of
data stores. The sparsity number K is indicative of the sparseness of the historical
survey data of the survey item in a particular domain, and corresponds to the
number of non-zero values in the data obtained after performing a predefined
mathematical transformation on the historical survey data from one domain to
another domain.
[0031] In an implementation, for determining the sparsity number K, the
historical survey data of one of the survey items for the T number of data stores in
distributed in a matrix. The distribution is such that each of the elements of the
matrix has the historical survey data of one survey item for one of the T number
of data stores. The matrix may be of an order of N1 x N2, where numbers N1 and
N2 are integers greater than zero such that the number N1 x N2 is equal to the
number T. In an example, the matrix may be a 2-dimension matrix, i.e., a square
or a rectangular matrix. In another example, the matrix may a 1-dimensional
matrix, where the number N1 may be equal to 1 and the number N2 may be equal
to T, or vice versa.
[0032] After the distribution of the historical survey data in the matrix, a
predefined mathematical transformation is performed on the matrix. The
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predefined mathematical transformation includes, but is not restricted to, Discrete
Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Cosine
Transform (DCT), Karhunen-Loève Transform (KLT), and such. By performing
the mathematical transformation, most of the values of elements of the matrix
may be zero or very close to zero. As a result, a sparseness of the historical survey
data in the matrix is emphasized after the mathematical transformation. For
determining the sparsity number K, number of elements, amongst the all elements,
of the transformed matrix having significant or non-zero values is determined.
The number of non-zero elements in the transformed matrix is the sparsity number
K for that survey item.
[0033] In an implementation, the sparsity number K is determined
individually for each of the survey items. Based on this, the lowest of the
determined sparsity numbers K is selected as the sparsity number K for further
processing in accordance with the present subject matter.
[0034] Further, in an implementation, the historical survey data of the
survey item for the T number of data stores may be distributed randomly in the
matrix. In an implementation, more than one matrix may be generated with the
historical survey data distributed randomly in different manners in each of the
matrices. Based on this, one of the matrices may be selected based on a level of
sparseness of the distributed historical survey data in the matrices, for determining
the sparsity number K.
[0035] In an implementation, for each of the survey items, the historical
survey data of the survey item for the T number of data stores may be distributed
in the matrix based on a lexicographical order of the T number of data stores with
a location proximity of the T number of data stores. For such a distribution, at
first, one data store in randomly selected from the T number of data stores, and
assigned or named with a number, for example, 1. Then, a data store, which is in
the closest proximity of the data store numbered as 1, is selected and named with
a number 2. Subsequently, a data store, which is in the second closest proximity of
the data store numbered as 1, is selected and named with a number 3. The
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procedure of selection and naming, based on the proximity of location, is repeated
for all the T number of data stores and the data stores are named till the number T.
[0036] After naming the data stores, the elements of the matrix, into which
the historical survey data for the T number of data stores is to be distributed, are
named with numbers from 1 to T. For example, consider a case where the matrix
is a rectangular matrix of the order N1 x N2. For naming the elements, starting
from the first element of the first row of the matrix, all the elements of the first
row are named with numbers from 1 to N1. Then, starting from the first element
of all the elements of the second row of the matrix, all the elements of the second
row are named with numbers from (N1 + 1) to (2 x N1). The procedure of naming
is done for the elements of all the rows of the matrix.
[0037] Now, after naming the data stores and naming the elements of the
matrix, the historical survey data of the survey item for the data store numbered ‘i’
is put in the element numbered ‘i’. With this distribution procedure, the historical
survey data of the proximally close data stores in the geographical area are
distributed close to each other.
[0038] After determining the sparsity number K, a target number M is
determined at block 106.The target number M is indicative of a reduced number
of data stores, from the T number of data stores, from which current survey data is
to be collected. The target number M may be of the order of the sparsity number
K times log T. In an implementation, the target number M is determined by
multiplying a multiplication factor s by the sparsity number K, i.e., M = s x K,
such that the target number M is in a range from about 0.3 times the number T to
about 0.5 times the number T. Here, the multiplication factor s can be equal to 1.5.
In an example, for the number T equal to 10000, the target number M may be in a
range from about 3500 to about 4500, depending on the sparsity number K.
[0039] Further, after determining the target number M, it may be checked,
at block 108, whether the survey is to be conducted for a single survey item or for
multiple survey items from the data stores. For conducting the survey of a single
product (‘Yes’ branch from block 108), M number of data stores are identified
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from the T number of data stores for data collection at block 110. The M number
of data stores may be identified randomly from the T number of data stores.
[0040] In an implementation, the random identification of M number of
data stores from the T stores may be performed using a random number generator.
The random number generator may be a conventional number generator
configured to generate numbers that lack any order or pattern. The random
number generator is used to generate M random numbers from 1 to the number T,
and the M number of data stores are identified based on the M random numbers.
For example, the data stores numbers corresponding to the M random numbers are
identified for the data collection.
[0041] After identifying the M number of data stores, current survey data
of the survey item for the M number of data stores is obtained at block 112. In an
implementation, the current survey data of the survey item for the M number of
data stores may be obtained through at least one of sensors and human surveyors.
The sensors may include computerized billing terminals, data-logging machines,
or a similar machine or device, depending on the type of data stores, which
collects and maintains a record of survey data. The survey data may be collected
on a daily-basis, a weekly-basis, monthly-basis, or such, for one or more survey
items for the data stores. The sensors may collect and transmit the survey data to a
central database at regular intervals (say, daily, weekly, monthly), through a
network. From the central database the current survey data of the survey item for
the identified M number of data stores may be obtained for further processing.
Further, for obtaining the current survey data through the human surveyors, the
human surveyors may have to manually visit the M number of data stores, and
collect or recorded the current survey data of the survey item in data log book(s)
or in hand-held data logging device(s). The collected data may then be reported
manually or via a network to a central database. For the purpose of further
processing, the current survey data may be directly obtained from the human
surveyors, or may be obtained from the central database, as the case may be.
[0042] The data stores may be located in rural areas, in semi-urban areas
or in urban areas. The current survey data for the survey item may be collected for
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M number of data stores using sensors or by human surveyors, or through a
combination both, depending on the availability of a network connection. In the
cases where the current survey data is collected using sensors, the process of data
collection may be automatic and non-intrusive in nature as it does not require
much human intervention. Thus, the collection of data can happen continuously in
a 24*7 manner. The sensors keep collecting data in the background and do not
affect the day-to-day operations at the data stores.
[0043] After obtaining the current survey data of the survey item for the M
number of stores, current survey data of the survey item for the T number of data
stores are estimated, at block 114, by performing reconstruction of data on the
current survey data for the M number of data stores. In an implementation, the
current survey data of the survey item for the T number of data stores may be
estimated or reconstructed using an L1-minimization computation on the obtained
current survey data of the survey item for the M number of data stores. The
concept of L1-minimization for reconstruction of more data from less data is
conventionally known, and, hence, not described in detail in the description
herein. In an implementation, the L1-minimization computation may be carried
out using conventional greedy computation processes, such as Basic Pursuit,
Orthogonal Matching Pursuit, and Homotopy.
[0044] The procedure of estimation of survey data, i.e., the current survey
data, is described above for one survey item. In an implementation, the procedure
for estimation of current survey data may be performed repeatedly and
individually for each product, in case the data is to be estimated for multiple
survey items.
[0045] In an implementation, for conducting the survey of multiple survey
items (‘No’ branch from block 108), M number of data stores are identified from
the T number of data stores for data collection at block 116. The M number of
data stores may be identified randomly from the T number of data stores in a
manner as described earlier in the description.
[0046] At block 118, historical survey data of the multiple survey items
for the M number of data stores are obtained. The historical survey data of the
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multiple survey items may be pre-recoded in data books of the data stores, or prestored
in a storage device of the data stores or in a central database. The historical
survey data may be obtained from at least one of the data books, the storage
device, and the central database.
[0047] After obtaining the historical survey data of the multiple survey
items for the M number of data stores, M' number of data stores are selected from
the M number of data stores based on a correlation between the historical survey
data of one or more pairs of the survey items at block 120. The number M' is an
integer less than the number M. The correlation may be based on the influence of
data of one survey item on the data of the other survey item in each pair.
[0048] In an example, where the data stores are retail stores and the survey
items are retail products offered by the retail stores, certain pairs of retail products
may be correlated in a manner that if a customer buys product A, then he is more
likely to buy product B to compliment product A. Examples of such pairs of retail
products may include pen and notebook, bread and jam, soap and deodorant,
shampoo and conditioner, and such. This correlation between retail of certain
products, or, in general, between the survey data of survey items, may facilitate in
exploiting the redundancy of survey data and in turn facilitate in reducing the total
number data stores from which current survey data may be collected. The number
of data stores may be reduced by dropping one or more data stores from the M
number of identified data stores. A data store may be dropped if one or more pairs
of survey items, from the multiple survey items at the data store, has a correlation
in the historical survey data.
[0049] Further, after selecting the M' number of data stores, current survey
data of the multiple survey items for the M' number of data stores are obtained at
block 122. In an implementation, the current survey data of the multiple survey
items for the M' number of data stores may be obtained through at least one of the
sensors and human surveyors in a manner described in detail earlier in the
description.
[0050] After obtaining the current survey data of the multiple survey items
for the M' number of stores, current survey data of the multiple survey items for
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the T number of data stores are estimated, at block 124, by performing
reconstruction of data on the current survey data for the M' number of data stores.
In an implementation, the current survey data of the each of the survey items for
the T number of data stores may be estimated or reconstructed using an L1-
minimization computation on the obtained current survey data of the survey items
for the M' number of data stores, as mentioned earlier in the description.
[0051] Further, in the cases where the current survey data is collected
using sensors, and the collected data is transmitted over a network to a central
database for further processing, some of the data may be lost during the
transmission. In an implementation, in order to prevent such data loss, the
collected data is encoded with real-field codes before the transmission of the data.
The central database, for example, a central server, may decode the received data
and store the current survey data therein. The encoding and decoding of the data
with real-field codes is known to a skillful person, and, thus, is not described in
detail in the description herein.
[0052] In an implementation, the data loss may be compensated through
an oversampling factor δ. In said implementation, the target number M is
increased by adding the oversampling factor δ. With this, the target number M is a
sum of the multiplication factor s times the sparsity number K and the
oversampling factor δ. Here, the oversampling factor δ is integer less than the
multiplication factor s times the sparsity number K (δ < s x K). Thus, with the
oversampling factor δ, the number of data stores from which the current survey
data is to be collected increases by a small number which guards against the loss
of data.
[0053] The method 100 may be used to conduct a survey for data stores
including, but not restricting to, retail stores, hospitals, and farming fields. In an
implementation, where the data stores are retail stores, the survey items include
retail products offered for sales by the retail stores. For said implementation, the
historical survey data include historical retail or sales data of the retail products,
and the current survey data include current retail or sales data of the retail
products.
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[0054] Figure 2 illustrates a network environment 200 implementing a
survey conducting system 202 for conducting a survey of at least one survey item,
according to an embodiment of the present subject matter. The network
environment 200 may be understood as a public or a private networking system.
As shown, the network environment 200 may include network terminals 204-1,
204-2, …, 204-n, collectively referred to as the network terminals 204 and
individually referred to as the network terminal 204. The current survey data of at
least one survey item for data stores may be collected through the network
terminal 204. The network terminals 204 may include sensors, such as billing
machines, point of sale terminals, data-logging devices, hand-held data logging
devices. The hand-held data logging devices may be communicating devices that
include, but are not limited to, mobile phones, smart phones, personal digital
assistants, tablets, and the like.
[0055] The type of network terminals 204 in the network environment 200
may depend on the type of data stores (not shown) for which the survey is to be
conducted. In an example, where the data stores are retail stores, the network
terminals 204 may include billing machines and point of sale terminals installed at
the retail stores, and may include hand-held data logging devices, through which
the current retail data is collected. In an example, where the data stores are
hospitals, the network terminals 204 may include data-logging devices installed at
the hospitals, and may include hand-held data logging devices, through which
current reported cases for a disease is collected. In another example, where the
data stores are farming fields, the network terminals 204 may include data-logging
devices and hand-held data logging devices through which current yield of a crop
is collected.
[0056] The network terminals 204 are coupled to a computing device 206
over a network 208 through one or more communication links for transmitting the
collected data. In an implementation, the survey conducting system 202 is
implemented in the computing device 206 for the purpose of conducting a survey
for the data stores. The computing device 206 may include, but is not limited to, a
server, a workstation, a mainframe computer, a desktop PC, a notebook, a portable
18
computer, and the like. The computing device 206 may be one, or combination of
one or more, storage server or network server.
[0057] In an implementation, as shown, the computing device 206 has a
database to store the collected current survey data transmitted by one or more of
the network terminals 204. In an implementation, the network terminals 204 may
transmit the collected current survey data to an external database, and the
computing device 206 with the survey conducting system 202 may
communicatively coupled with such database for obtaining the collected current
survey data.
[0058] Further, in an implementation, network environment 200 may
include human surveyor 210-1, 210-2, …, 210-n, collectively referred to as the
human surveyors 210 and individually referred to as the human surveyor 210. The
human surveyors 210 may collect the current survey data from the data stores (not
shown) and report the collected data to the computing device 206 with the survey
conducting system 202.
[0059] The network 208 may be understood as a network, including
personal computers, laptops, various servers and other computing devices. The
communication links between the network terminals 204 and the computing
device 206 are enabled through a desired form of communication, for example,
via dial-up modem connections, cable links, and digital subscriber lines (DSL),
wireless or satellite links, or any other suitable form of communication.
[0060] Further, the network 208 may be a wireless network, a wired
network, or a combination thereof. The network 208 can also be an individual
network or a collection of many such individual networks, interconnected with
each other and functioning as a single large network, e.g., the Internet or an
intranet. The network 208 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 such. The network 208 may either be a dedicated network or a
shared network, which 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), etc., to communicate
19
with each other. Further, the network 208 may include network devices, such as
network switches, hubs, routers, and Host Bus Adapters (HBAs), for providing a
link between the network terminals 204 and the computing device 206. The
network devices within the network 208 may interact with the network terminals
204 and the computing device 206 through the communication links.
[0061] The survey conducting system 202 includes one or more
processor(s) 212, interface(s) 214, a memory 218, module(s) 220, and data 222,
coupled to the processor(s) 212. The processor(s) 212 can be a single processor
unit or a number of units, all of which could include multiple computing units.
The processor(s) 212 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 processor(s) 212
is configured to fetch and execute computer-readable instructions and data stored
in the memory 218.
[0062] Functions of the various elements shown in Figure 2, including the
functional blocks labeled as “processor(s)”, may be provided through the use of
dedicated hardware as well as hardware capable of executing software in
association with appropriate software. When provided by a processor, the
functions may be provided by a single dedicated processor, by a single shared
processor, or by a plurality of individual processors, or by a plurality of subprocessors.
Moreover, explicit use of the term “processor” should not be
construed to refer exclusively to hardware capable of executing software, and may
implicitly include, with a limitation, Digital Signal Processor (DSP) hardware,
network processor, Application Specific Integrated Circuit (ASIC), Field
Programmable Gate Array (FPGA), Read Only Memory (ROM) for storing
software, Random Access Memory (RAM), and non-volatile storage. Other
hardware, conventional or custom, may also be included. Further, the processor(s)
212 may include various hardware components, such as adders, shifters, sign
correctors, and generators required for executing various applications, such as
arithmetic operations.
20
[0063] The interface(s) 214 may include a variety of software and
hardware interfaces, for example, interfaces for peripheral device(s), such as a
keyboard, a mouse, an external memory, and a printer. The interface(s) 214 may
enable the survey conducting system 202 to communicate with other devices, such
as external computing devices and external databases.
[0064] The memory 218 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.
[0065] The module(s) 220 include routines, programs, objects,
components, data structures, and the like, which perform particular tasks or
implement particular abstract data types. The module(s) 220 further include
modules that supplement applications on the survey conducting system 202, for
example, modules of an operating system. The data 222, amongst other things,
serves as a repository for storing data that may be processed, received, or
generated by one or more of the modules 220.
[0066] In an implementation, the modules 220 of the survey conducting
system 202 include a computation module 224, a store identification module 226,
a data fetching module 228, and other module(s) 230. The other module(s) 230
may include programs or coded instructions that supplement applications and
function, for example, programs in the operating system of the survey conducting
system 202.
[0067] In an implementation, the data 222 include estimated data 232,
collected data 234, historical data 236, and other data 238. The other data 238
includes data generated as a result of the execution of one or more modules in the
other module(s) 230.
[0068] The computation module 224 is configured to obtain historical
survey data of at least one survey item for T number of data stores. The historical
survey data may be pre-stored in the survey conducting system 202, or may be
21
obtained from an external database (not shown) and stored in the historical data
236.
[0069] Based on the historical survey data of the at least one survey item
for the T number of data stores, the computation module 224 determines a sparsity
number K associated with the historical survey data. In an implementation, for
determination of the sparsity number K, the historical survey data is distributed in
a matrix, and a predefined mathematical transformation is performed on the
matrix. The predefined mathematical transformation includes, but is not restricted
to, DFT, DWT, DCT, KLT, and such. The number of elements in the transformed
matrix having non-zero values is determined as the sparsity number K. The matrix
is of an order of N1 x N2, where the number N1 x N2 is equal to the number T.
[0070] In an implementation, the historical survey data of each of the
survey items for the T number of stores is distributed randomly in a matrix. The
distribution is such that each of the elements of the matrix has the historical
survey data of one survey item for one of the T number of data stores. Also, in an
implementation, the historical survey data of a survey item for the T number of
data stores may be distributed in the matrix based on a lexicographical order of
the T number of data stores with a location proximity of the T number of data
stores, as described earlier in the description.
[0071] Further, based on the determined sparsity number K, the
computation module 224 determines a target number M. The target number M is
indicative of a reduced number of data stores, from the T number of data stores,
for the collection of current survey data to estimate current survey data of the at
least one survey item for the T number of data stores. Further, in an
implementation, the target number M is determined by multiplying a
multiplication factor s by the sparsity number K, i.e., M = s x K, such that the
target number M is in a range from about 0.3 times the number T to about 0.5
times the number T. The multiplication factor s is equal to 1.5.
[0072] Subsequent to determining the target number M, if the survey is to
be conducted for a single survey item, the store identification module 226
identifies M number of data stores randomly from the T number of data stores for
22
the data collection. In an implementation, the store identification module 226 may
be configured to identify the M number of data stores from the T number of data
stores using a random number generator in a manner described earlier in the
description.
[0073] After identifying the M number of data stores, the data fetching
module 228 fetches or obtains current survey data of the survey item for the M
number of data stores. In an implementation, the data fetching module 228 may
fetch the current survey data of the survey item from at least one of the network
terminals 204 and the human surveyors 210. In the cases where the data is
collected by the human surveyors 210 or the network terminals 204, such as handheld
devices or data logging devices operated by a human surveyor, the manpower
requirement is substantially reduced as a small number, i.e., the M number, of data
stores is to be visited by the human surveyors 210. In the cases where the data is
collected using the sensors, the amount of data to be transmitted over the network
208 is substantially reduced as the data is to be collected from a small number, i.e.,
the M number, of data stores. This substantially reduces the bandwidth
requirements of the network 208. The current survey data is stored in the collected
data 234.
[0074] Further, based on the fetched current survey data of the survey item
for the M number of data stores, the computation module 224 estimates current
survey data of the survey item for the T number of data stores. As mentioned
earlier, in an implementation, the current survey data of the survey item for the T
number of data stores may be estimated by the computation module 224 through
the reconstruction of data from the fetched current survey data of the survey item
for the M number of data stores. The data may be reconstructed using an L1-
minimization computation as mentioned earlier in the description. Further, in an
implementation, the survey conducting system 202 uses a platform, such as
MATLAB, C/C++, Java, or the like, for carrying out the L1-minimization
computation. The reconstructed or the estimated current survey data of the survey
item for the T number of data stores may be further used or processed for a variety
of purposes. The estimated current survey data is stored in the estimated data 232.
23
[0075] Now, subsequent to determining the target number M, if the survey
is to be conducted for multiple survey items, the store identification module 226
identifies M number of data stores randomly from the T number of data stores for
the data collection. After identifying the M number of data stores, the computation
module 224 obtains historical survey data of the multiple survey items for the M
number of data stores. The historical survey data may be pre-stored in the survey
conducting system 202, or may be obtained from an external database (not shown)
and stored in the historical data 236. Based on the historical survey data, the
computation module 224 identifies a correlation between the historical survey
data of one or more pairs of the multiple survey items. The correlation may be
based on the influence of data of one product on the data of the other product in
each pair.
[0076] After identifying the correlation in the historical survey data of the
multiple survey items, the store identification module 226 selects M' number of
data stores from the M data stores based on the correlation. In an implementation,
the number M' is an integer less than the number M. For selecting the M' number
of data stores, the number of data stores may be reduced by dropping one or more
data stores from the M number of data stores identified for data collection. As
mentioned earlier, a data store may be dropped if one or more pairs of survey
items, from the multiple survey items, has a correlation.
[0077] Further, after selecting the M' number of data stores, the data
fetching module 228 fetches or obtains current survey data of the multiple survey
items for the M' number of data stores. The current survey data of the multiple
survey items for the M' number of data stores may be fetched or obtained from the
network terminals 204 and/or from the human surveyors 210 as mentioned earlier
in the description.
[0078] Based on the fetched current survey data for the M' number of data
stores, the computation module 224 estimates current survey data of the multiple
survey items for the T data stores. Again, the computation module 224 may
perform the L1-minimization computation on the current survey data of the
multiple survey items for the M' number of data stores to reconstruct and estimate
24
the current survey data of the multiple survey items for the T number of data
stores.
[0079] In an implementation, the computation module 224 is configured to
perform a check on whether the survey is to be conducted for a single survey item
or for multiple survey items. In an implementation, the computation module 224
may receive an input from a user operating the survey conducting system 202 for
making such a choice.
[0080] The survey conducting system 202 may be used to conduct a
survey for data stores including, but not restricting to, retail stores, hospitals, and
farming fields. In an implementation, where the data stores are retail stores, the
survey items include retail products offered for sales by the retail stores. For said
implementation, the historical survey data include historical retail or sales data of
the retail products, and the current survey data include current retail or sales data
of the retail products.
[0081] Although embodiments for the method and system for conducting
a survey have been described in language specific to structural features, it is to be
understood that the invention is not necessarily limited to the specific features
described. Rather, the specific features are disclosed and explained in the context
of a few embodiments for the method and system.
[0082] Other advantages of the method and system of the present subject
matter will become better understood from the description and claims of an
exemplary embodiment of the method and system. The method and system of the
present subject matter are not restricted to the embodiments that are mentioned
above in the description.
[0083] Although the subject matter has been described with reference to
specific embodiments, this description is not meant to be construed in a limiting
sense. Various modifications of the disclosed embodiments, as well as alternate
embodiments of the subject matter, will become apparent to persons skilled in the
art upon reference to the description of the subject matter. It is therefore
contemplated that such modifications can be made without departing from the
spirit or scope of the present subject matter as defined.
25
I/We Claim:
1. A method for conducting a survey of at least one survey item, the method
comprising:
obtaining historical survey data of the at least one survey item for T
number of data stores;
determining a sparsity number K associated with the historical survey
data of the at least one survey item for the T number of data stores; and
determining a target number M based on the sparsity number K,
wherein the target number M is indicative of a reduced number of data
stores, present amongst the T number of data stores, for collection of
current survey data to estimate current survey data of the at least one
survey item for the T number of data stores.
2. The method as claimed in claim 1 further comprising:
identifying M number of data stores randomly from the T number of
data stores for the collection of the current survey data;
obtaining current survey data of the at least one survey item for the M
number of data stores; and
estimating current survey data of the at least one survey item for the T
number of data stores based on the current survey data for the M number
of data stores.
3. The method as claimed in claim 1 further comprises:
identifying M number of data stores randomly from the T number of
data stores;
obtaining historical survey data of multiple survey items for the M
number of data stores;
selecting M' number of data stores from the M number of data stores
based on a correlation between the historical survey data of one or more
pairs of the survey items, wherein the number M' is integer less than the
number M;
26
obtaining current survey data of the multiple survey items for the M'
number of data stores; and
estimating current survey data of the multiple survey items for the T
number of data stores based on the current survey data for the M' number
of data stores.
4. The method as claimed in any one of the claims 2 and 3, wherein the
estimating the current survey data for the T number of data stores is based on
an L1-minimization computation on the current survey data obtained.
5. The method as claimed in claim 1 further comprising:
distributing the historical survey data of each of the at least one survey
item for the T number of data stores as values of elements of a matrix of
an order of N1 x N2, wherein N1 and N2 are integers greater than zero
such that N1 x N2 is equal to the number T, and each of the elements of
the matrix has the historical survey data of one survey item for one of the
T number of data stores, wherein,
the determining of the sparsity number K is based on number of
elements of the matrix, amongst all the elements of the matrix, having
non-zero values after performing one of Discrete Fourier Transform,
Wavelet Transform, Discrete Cosine Transform (DCT), and Karhunen-
Loève Transform on the matrix.
6. The method as claimed in claim 5, wherein the distributing the historical
survey data of each of the at least one survey item for the T number of data
stores is based on a lexicographical order of the T number of data stores with a
location proximity of the T number of data stores.
7. The method as claimed in claim 1, wherein the target number M is a
multiplication factor s times the sparsity number K, such that the target
number M lies between about 0.3 times the number T to about 0.5 times the
number T.
8. The method as claimed in claim 1, wherein the target number M is a sum of a
multiplication factor s times the sparsity number K and an oversampling factor
27
δ, wherein the oversampling factor δ is less than the multiplication factor s
times the sparsity number K.
9. The method as claimed in any one of the claims 7 and 8, wherein the
multiplication factor s is 1.5.
10. A survey conducting system (202) comprising:
a processor (212);
a computation module (224) coupled to the processor (212), the
computation module (224) is configured to,
obtain historical survey data of at least one survey item for T
number of data stores; and
determine a sparsity number K based on performing a predefined
mathematical transformation on the historical survey data of the at
least one survey item for the T number of data stores; and
determine a target number M based on the sparsity number K,
wherein the target number M is indicative of a reduced number of data
stores, present amongst the T number of data stores, for collection of
current survey data to estimate current survey data of the at least one
survey item for the T number of data stores.
11. The survey conducting system (202) as claimed in claim 10 further comprises:
a store identification module (226) coupled to the processor (212), the
store identification module (226) is configured to identify M number of
data stores randomly from the T number of data stores for the collection of
current survey data; and
a data fetching module (228) coupled to the processor (212), the data
fetching module (228) is configured to fetch current survey data of the at
least one survey item for the M number of data stores, wherein,
the computation module (224) is configured to estimate current
survey data of the at least one survey item for the T number of data
stores based on the current survey data for the M number of data stores.
28
12. The survey conducting system (202) as claimed in claim 10 further
comprising:
a store identification module (226) coupled to the processor (212), the
store identification module (226) is configured to identify M number of
data stores randomly from the T number of data stores for the collection of
current survey data, wherein,
the computation module (224) is configured to,
obtain historical survey data of multiple survey items for the M
number of data stores; and
identify a correlation between the historical survey data of one
or more pairs of the multiple survey items.
13. The survey conducting system (202) as claimed in claim 12, wherein the store
identification module (226) is configured to select M' number of data stores
from the M number of data stores based on the correlation, wherein the
number M' is integer less than the number M.
14. The survey conducting system (202) as claimed in claim 13 further
comprising a data fetching module (228) coupled to the processor (212), the
data fetching module(228) is configured to fetch current survey data of the
multiple survey items for the M' number of data stores, and wherein,
the computation module (224) is configured to estimate current survey
data of the multiple survey items for the T number of data stores based on
the current survey data for the M' number of data stores.
15. The survey conducting system (202) as claimed in any one of the claims 11
and 14, wherein the current survey data is fetched from at least one of a sensor
and a human surveyor.
16. The survey conducting system (202) as claimed in claim 10, wherein the
computation module (224) is configured to,
distribute the historical survey data of each of the at least one survey
item for the T number of data stores as values of elements of a matrix of
an order of N1xN2, wherein N1 and N2 are integers greater than zero such
29
that N1xN2 is equal to the number T, and each of the elements of the
matrix has the historical survey data of one survey item for one of the T
number of data stores; and
determine, for each of the at least one survey item, the sparsity number
K based on number of elements of the matrix, among all the elements of
the matrix, having non-zero values after performing the predefined
mathematical transformation on the matrix
17. The survey conducting system (202) as claimed in claim 10, wherein the
predefined mathematical transformation comprises one of Discrete Fourier
Transform, Discrete Wavelet Transform, Discrete Cosine Transform (DCT),
and Karhunen-Loève Transform.
18. The survey conducting system (202) as claimed in claim 10, wherein the target
number M is a multiplication factor s times the sparsity number K, such that
the target number M is in a range from about 0.3 times the number T to about
0.5 times the number T.
19. A method for conducting a survey of at least one retail product, the method
comprising:
obtaining historical retail data of the at least one retail product for T
number of retail stores;
determining a sparsity number K associated with the historical retail
data of the at least one retail product for the T number of retail stores; and
determining a target number M based on the sparsity number K,
wherein the target number M is indicative of a reduced number of retail
stores, from the T number of retail stores, for collection of current retail
data to estimate current retail data of the at least one retail product for the
T number of retail stores.
20. A survey conducting system (202) comprising:
a processor (212);
a computation module (224) coupled to the processor (212), the
computation module (224) is configured to,
30
obtain historical retail data of at least one retail product for T
number of retail stores; and
determine a sparsity number K based on performing a predefined
mathematical transformation on the historical retail data of the at least
one retail product for the T number of retail stores; and
determine a target number M based on the sparsity number K,
wherein the target number M is indicative of a reduced number of
retail stores, from the T number of retail stores, for collection of
current retail data to estimate current retail data of the at least one
retail product for the T number of retail stores.
21. A computer-readable medium having computer-executable instructions that
when executed perform acts comprising:
obtaining historical survey data of the at least one survey item for T
number of data stores;
determining a sparsity number K associated with the historical survey
data of the at least one survey item for the T number of data stores; and
determining a target number M based on the sparsity number K,
wherein the target number M is indicative of a reduced number of data
stores, present amongst the T number of data stores, for collection of
current survey data to estimate current survey data of the at least one
survey item for the T number of data stores.