Optimising way for connecting farmers to mobile world.
Manisha Shinde-Pawar
Assistant Professor, Dept. of Management, IMRDA, SANGLI, Bharati Vidyapeeth Univerisity, Pune, India
ABSTRACT: To motivate farmer’s participation in expanded market opportunities easily accessible ways should be offered. Mobile devices communication is the best communication way opportunity among all different communication ways, for all type of communication based operation to reach to larger number of users. To reduce poverty and to ensure sustainable livelihood to farmers who are major part of population living in poverty and without livelihood security, proper measures and accordingly technology implementation need to be redesigned.
The researcher would like to suggest a solution for rural services and processes by rebuilding and optimising agricultural governance with m-governance to take advantage to reach number of farmers through mobile devices. This approach will need wireless services and mobile network services infrastructural rebuilding in rural areas to as to meet needs.
Keyword: Agricultural Governance, Mobile network, m- governance, Rural Area, Wireless services.
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I. INTRODUCTION
1. Mobile Agriculture (also shortened to mAgri) supports actors along the agriculture value chain through the use of mobile technology. Mobile technology covers a broad range of devices and the sub-categories include voice, data, network and connectivity technologies. mAgri is a subset of e-agriculture.
2. m-Government :
m-Government refers to collection of services as the strategic use of government services and applications which are only possible using cellular / mobile telephones, laptop computers, personal digital assistants (PDAs) and wireless internet infrastructure.
3. Benefits:
· Cost reduction
· Efficiency
· Transformation/modernization of public sector organizations
· Added convenience and flexibility
· Better services to the citizens
· Ability to reach a larger number of people through mobile devices than would be possible using wired internet only
4. Cases:
· India's Ministry of Communication and Information Technology, Department of Information Technology (DoIT) has announced plans for all its department and agencies to develop and deploy mobile applications to provide all their services through mobile devices. Following are the main measures laid down by DoIT:
· Web sites of all Government Departments and Agencies shall be made mobile-compliant, using the “One Web” approach.
· Open standards shall be adopted for mobile applications for ensuring the interoperability of applications across various operating systems and devices as per the Government Policy on Open Standards for e-Governance.
· Uniform/ single pre-designated numbers (long and short codes) shall be used for mobile-based services to ensure convenience.
· All Government Departments and Agencies shall develop and deploy mobile applications for providing all their public services through mobile devices to the extent feasible on the mobile platform. They shall also specify the service levels for such services.
· To ensure adoption and implementation of the framework in time bound manner the government will develop Mobile Service Delivery Gateway (MSDG) that is the core infrastructure for enabling the availability of public services in through mobile devices.
5. Services:
· Wireless and mobile networks and related infrastructure, as well as software, must be developed
· To increase citizen participation and provide citizen-oriented services, governments need to offer easy access to mGovernment information in alternative forms
· Mobile phone numbers and mobile devices are relatively easily hacked and wireless networks are vulnerable because they use public airwaves to send signals
· Many countries have not yet adopted legislation for data and information practices that spell out the rights of citizens and the responsibilities of the data holde
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II. LITERATURE REVIEW
improving net income of farmers through increased production, better prices, and support from government through improvement of land, water and services. Table No.1 comparative Probabilistic Data Structure Implementations for Big Data
Sr. No. / Data Structure / Big Data Implementation Level1 /
Linear Counting
/ i. A liner counter is just a bit set and each element in the data set is mapped to a bit.2 /
Loglog Counting
/ i. More powerful and much more complex techniqueii. Unstable estimation determined by using multiple independent observations and averaging them.
iii. Incoming values are routed to a number of buckets by using their first bits as a bucket address. Each bucket maintains a maximum rank of the received values
3 /
Count-Min Sketch
/ i. A family of memory efficient data structures that allow one to estimate frequency-related properties of the data set.ii. Count-Min algorithm estimates frequency of the given value as a minimum of the corresponding counters in each row because the estimation error is always positive
4 /
Count-Mean-Min Sketch
/ i. performs well on highly skewed dataii. It estimates noise for each hash function as the average value of all counters in the row that correspond to this function (except counter that corresponds to the query itself), deduces it from the estimation for this hash function, and, finally, computes the median of the estimations for all hash functions. Having that the sum of all counters in the sketch row equals to the total number of the added elements
5 /
Stream-Summary
/ i. Stream-Summary allows one to detect most frequent items in the dataset and estimate their frequencies with explicitly tracked estimation error.ii. Stream-Summary traces a fixed number (a number of slots) of elements that presumably are most frequent ones. If one of these elements occurs in the stream, the corresponding counter is increased. If a new, non-traced element appears, it replaces the least frequent traced element and this kicked out element become non-traced.
iii. Stream-Summary groups all traced elements into buckets where each bucket corresponds to the particular frequency, i.e. to the number of occurrences.
6 /
Array of Count-Min Sketches
/ i. Range query (something like SELECT count(v) WHERE v >= c1 AND v < c2) using a Count-Min sketch enumerating all points within a range and summing estimates for corresponding frequencies.ii. maintain a number of sketches with the different “resolution”, i.e. one sketch that counts frequencies for each value separately, one sketch that counts frequencies for pairs of values (to do this one can simply truncate a one bit of a value on the sketch’s input), one sketch with 4-items buckets and so on.
7 /
Bloom Filter
/ i. Most famous and widely used probabilistic data structure.ii. Bloom filter is similar to Linear Counting, but it is designed to maintain an identity of each item rather than statistics. Similarly to Linear Counter, the Bloom filter maintains a bitset, but each value is mapped not to one, but to some fixed number of bits by using several independent hash functions.
iii. If the filter has a relatively large size in comparison with the number of distinct elements, each element has a relatively unique signature and it is possible to check a particular value – is it already registered in the bit set or not. If all the bits of the corresponding signature are ones then the answer is yes.
iv. A query returns either "possibly in set" or "definitely not in set".
The above table no. 1 shows comparative analysis of different probabilistic algorithms for big data structures and what it takes into account for implementation base.
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III. STATEMENT OF THE PROBLEM
As information technology system become less monolithic and more distributed, real-time big data analysis will become less exotic and more common place. At that point, the focus will shift from data science to next logical frontier: decision science.
Researches would like to focus big data by applying data structure for the study to design an efficient, iterative smart algorithmic data model for unstructured big data. Such algorithm is very useful to store, retrieve and search and analyses big data.
IV. OBJECTIVES
· To carry out comparative study of different data structures applied to big data.
· Design Iterative Smart Algorithmic data model for unstructured big data.
· Generalization of Algorithm.
· While considering multiple parameters, with accuracy, expected to be fast, precise and improved. It will help to design the strategies and reduce the business loss.
· The techniques will be generalized, useful not only for Indian Educational System, but for any area wherever voluminous data is required to be accessed within shortest time.
V. RESEARCH METHODOLOGY
The researcher has planned to follow Design and Creation research Strategy (figure no. 2). The strategy focuses on formation of new xml based processing technique for big data analytics.
Figure No. 2. XML Based Processing of Big Data
As shown in Figure No. 2, unstructured big data can be collected and integrated together in XML Object Data structure. The researcher would like to explore the strength of searching, sorting and processing of XML Structures. XML Structures aimed to extract relevant information from possible set of heterogeneous documents in form of unstructured text. All data users may not be capable to read and analyses information from many varieties of structured data. All Such different data sources are also integrated to get combination of partially structured data and structured data. XML Tress, sub trees or graph structures in form of XML object can extends XML Document Structure and XML documents can be scanned for matching XML pattern. Such data structure design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science.
Figure No. 3. XML Based Processing of Big Data
As shown the figure no. 3, the approach aimed XML based processing of big data involving three different stages to transform less structured data to highly structured as
i. Collection of Distributed Data
ii. Transform it to XML Object Data Structure
iii. Store to/retrieve from Database
Figure No.3 shows that, Distributed File Systems may have one of the form of structured data but because of heterogamous structure forms, user may not analyse it with accuracy and speed. It involves integration of heterogamous structured data and less structured data and unstructured data with XML structure so that XML objects can be stored and retrieved with possibly universal format. Grammar based XML structure can help to decompose and synthesise sentence from document so as to analyse the opinion, so it will help to efficient and significant analytic development.
Figure No. 4 shows that, it needs to simplify the large and complex data sets into smaller but logically related container sets, so all documents firstly will be divided into opinionated and non-opinionated documents, so as to focus only opinionated documents to synthesize furthermore. The researcher would like to apply universal data structure to store and retrieve big data.
Then subjective and objective sentences of documents can be compared using automated rule based system to make predictions and to suggest decisions. Predicted results can be classified into three categories as positive, negative and neutral set of opinions.
Figure No. 4 Classification of XML Structure Big Data
XML has proved its power in so many different area. As Elements and attribute values can be stored and also can be transmitted by using XML, software development stakeholders are enjoying the way of XML implementations.
Data node are always leaf nodes in XML tree structure, but as shown in figure no. 3, Big Data can be classified further to have more levels of node to represent data according to opinionated data and non-opinionated data tree nodes. And Opinionated Document element will have hierarchy will have further levels of subjective and Objective Document nodes.
At XML tree as big data object data structure, sentence can be decomposed with Grammar Rule bases to get more precise nodes to reach to leaf node. Objective node is classified as positive, negative and neutral node.
VI. CONCLUSION
In this paper, by XML object as universal and very simple data structure. The linearization of tree structure which is having namespaces, expressiveness with structured grammar for regular grammar with International Unicode Standard, XSLT, XSL formatting objects, XQuery specification provides flexible structure, strong but simple tree structure. XSLT is stronger in its handling of narrative documents with more flexible structure, while XQuery is stronger in its data handling
Proposed XML tree structure aimed to design appropriate data structure for big data handling and to use it so as to analyse and retrieve data in meaningful way to take right decisions at right time.
VII. REFERENCES
1. Edd Dumbill, Big Data 2012 Edition O’Reilly, Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA
2. Soumendra Mohanty, Madhu Jagadeesh, Harsha Srivatsa ,”Big Data Imperatives: Enterprise ‘Big Data’ Warehouse, BI Implementations and Analytics” Apress
3. Vincenzo Loia, Masoud Nikravesh, Lofti A. Zadeh, Studies in Fuzziness and Soft Computing, “ Fuzzy Logic and Internet”, Springer-Verlag Berlin Heidelberg New York, ISBN-3-540-20180-7.
4. Felipe Bravo-Marqueza, Marcelo Mendoza, Barbara Poblete “Meta-Level Sentiment Models for Big Social Data Analysis”, Knowledge Based Systems May 2014
5. Demystifying Big Data: A Practical Guide to Transforming the Business of Government, TechAmerica Foundation’s Federal Big Data Commission, 2012
6. George Gilbert, A guide to big data workload management challenges, May 2012, by Datastax.
7. Manisha Shinde, “Formation of Smart Sentiment Analysis Technique for Big Data”, International Journal of Innovative Research in Computer and Communication Engineering. Vol.2, Issue 12, December 2014.
8. “Probabilistic Data Structures for Web Analytics and Data Mining” posted on May 1st, 2012, https://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining
9. http://en.wikipedia.org/wiki/Mobile_agriculture
10. http://en.wikipedia.org/wiki/M-government
Note: All Internet references are active as on 2nd January, 2015.
BIOGRAPHY
Manisha Shinde-Pawar received the B.Sc. in Computer Science degree in 2003 and MBA degree in Information of Technology and Management in 2008. . She has also received Master of Computer Application (MCA). She has joined BVDU, IMRDA, Sangli, MS, India as Assistant Professor in Information Technology. Her research interests are distributed systems and mobile computing, big data etc.
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