Abstract: System and method for contract management in a data marketplace are disclosed. In an embodiment, the system performs refactoring of a contract, during which the system extracts terms and conditions from the contract and generates a simplified view of the contract. The system further performs a requirement validation based on the contract, during which the system determines features of data entity matches requirements specified by a first party or not, based on domain specific ontologies. If the data entity features are not matching with the requirements, then the system fetches one or more relevant attributes from a list of ontologies, verifies whether the features of entity along with the selected feature(s) satisfy the requirements or not. The system accordingly generates an agreeable requirement document as output of the requirement validation.

Abstract: Systems and methods for computing data privacy-utility tradeoff is disclosed. Large data hubs like data marketplace are a source of data that may be of utility to data buyers. However, output data provided to data sellers is required to meet the privacy requirements of data sellers and at the same time maintain a level of utility to data buyers. Conventionally known methods of achieving data privacy tend to suppress components of data that may result in reduced utility of the data. Systems and methods of the present disclosure compute this tradeoff to establish need for data transformation, if any, before data is shared with data sellers.

Abstract: System and method for contract management in a data marketplace are disclosed. In an embodiment, the system performs refactoring of a contract, during which the system extracts terms and conditions from the contract and generates a simplified view of the contract. The system further performs a requirement validation based on the contract, during which the system determines features of data entity matches requirements specified by a first party or not, based on domain specific ontologies. If the data entity features are not matching with the requirements, then the system fetches one or more relevant attributes from a list of ontologies, verifies whether the features of entity along with the selected feature(s) satisfy the requirements or not. The system accordingly generates an agreeable requirement document as output of the requirement validation.

Abstract: This disclosure relates generally to a system and method to predict flight delay. Moreover, the embodiments herein further provide the system and method to predict timings of an airline in real time by considering historical operations (arrival and departure) data, historical airport data (captured at the time of arrival and departure) including congestion information, and weather data of the airport. The flight delays involves prediction of arrival and departure times of flight. Herein, the method categorizes input data related to an airline history, airline network, airport data and various airline reference data. Further, the method analyses the cause of delay which may be due to maintenance issues with the aircraft, fueling, weather, congestion in air traffic, and security issues etc. The system and method computes the flight delay due to multiple airline operations and different input datasets using stochastic approximation approach.

Abstract: Conventional systems and methods for order management are not geared to address varying and modifiable attributes of data products which may lead to conflicts that need to be resolved for a trade to conclude. Systems and methods are provided for resolving such conflicts prevalent in voluminous data hubs such as data marketplaces associated with buy orders and sell orders including metadata associated with product data, terms and conditions and price attributes. The conflict resolution provided is an automated and streamlined process that takes into account basic requirements of buyers and sellers along with a comprehensive resolution of conflicts that may arise when meeting the privacy requirements associated with data being traded, contract requirements proposed or concluded for the data being traded, reputation score associated with the trading parties and price discovery based on the variations in trading mechanisms possible in huge data marketplace.

Abstract: Systems and methods for computing data privacy-utility tradeoff is disclosed. Large data hubs like data marketplace are a source of data that may be of utility to data buyers. However, output data provided to data sellers is required to meet the privacy requirements of data sellers and at the same time maintain a level of utility to data buyers. Conventionally known methods of achieving data privacy tend to suppress components of data that may result in reduced utility of the data. Systems and methods of the present disclosure compute this tradeoff to establish need for data transformation, if any, before data is shared with data sellers.

Abstract: A method and system is provided for allocating a suitable price discovery mechanism in a data marketplace. The system takes a set of requirements from one or more buyers and a set of specifications for the data products from one or more sellers. The matching is performed on the set of requirements and the set of specifications of the data products to determine whether data transaction should be proceeded or not. The output is then provided to the classification module to classify the data marketplace to choose the most suitable price discovery mechanism which can be used for a particular data transaction in the data marketplace. The system can use of any of the following price discovery techniques. Bid order matching, auctioning or direct negotiation. Once the price is finalized, the finalized price then can be send to an order management module of the data marketplace.

Abstract: A method and system is provided for managing complaint and reputation in a multi-party data marketplace. The system is managed by an independent external entity to monitor the data transactions in an unbiased manner. The system considers the data as commodity or resource, which is perishable and whose worth might decay with time. The system defines new parameters for reputation and liability calculation (based on complaints), for example, consideration of peer and trust network, history of peering and transaction, automatic decay of reputation and liability in case of inactive participants. According to another embodiment, the disclosure also handles any kind of collusion between external or internal entities/parties/participants. Whereas in another embodiment the disclosure also identifies and restrict influencers in a multi-party data marketplace.

Abstract: A system and method for quantification of Escherichia Coli bacteria in water is disclosed. In an embodiment, a region of interest (ROI) is obtained from an image of the water. For example, the ROI includes a plurality of pixels in the image of water contaminated with Escherichia Coli bacteria. Further, a plurality of red pixels are identified from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, total redness of the plurality of red pixels in the ROI is calculated based on intensity of plurality of red pixels. In addition, a redness factor indicative of a degree of redness of the ROI is computed based on the calculated total redness. Quantification of the Escherichia Coli bacteria is then estimated based on the computed redness factor.

Abstract: Method and system for determining the number of computing resources for executing a plurality of jobs. In accordance with the method and the system, the plurality of jobs along with a plurality of variables associated to the plurality of jobs may be received. The plurality of variables may include a mean processing time (?), a standard deviation (?), a service level agreement (?), and a confidence level (1-?). After the receipt of the plurality of variables, an inverse parameter (?) may be determined using the mean processing time (?), the standard deviation (?), and the confidence level (1-?). Further, a queue length (k) may be computed based upon the total processing time (T), the mean processing time (?), the standard deviation (?), and the inverse parameter (?). Finally, the queue length (k) and the number of jobs (n) may be processed in order to determine the number of computing resources.

Abstract: Method and system for determining the total processing time required for executing a plurality of jobs. In certain aspects, a set of jobs along with a mean processing time and a queue length are received. The set of jobs are distributed across computing resources based upon the queue length. A set of priority jobs is received when the execution of a subset of jobs from the set of jobs has been executed in a first processing time (TF). Further, the set of priority jobs is distributed for execution across the plurality of computing resources. A second processing time (TS) required for the execution of the set of priority jobs is computed. Further, a third processing time (TT) required for the execution of the set of remaining jobs from the set of jobs is determined. Finally, the total processing time is determined by summing TF, TS, and TT.

Abstract: A system and method for quantification of Escherichia Coli bacteria in water is disclosed. In an embodiment, a region of interest (ROI) is obtained from an image of the water. For example, the ROI includes a plurality of pixels in the image of water contaminated with Escherichia Coli bacteria. Further, a plurality of red pixels are identified from the ROI based on a value of the plurality of pixels in the ROI and a threshold value. Furthermore, total redness of the plurality of red pixels in the ROI is calculated based on intensity of plurality of red pixels. In addition, a redness factor indicative of a degree of redness of the ROI is computed based on the calculated total redness. Quantification of the Escherichia Coli bacteria is then estimated based on the computed redness factor.

Abstract: Method and system for determining the total processing time required for executing a plurality of jobs (n). The plurality of jobs along with a mean processing time (?), and a queue length (k) are received. A number of preliminary jobs (p) of the plurality of jobs (n) are distributed across computing resources based upon the queue length (k). A first processing time (TF) required for the execution of the number of preliminary jobs (p) is determined. Further, a number of remaining jobs (n?p) of the plurality of jobs (n) are distributed for execution across the plurality of computing resources. A second processing time (TS) required for the execution of the set of remaining jobs (n?p) is computed. Finally, the total processing time is determined by summing TF and TS.

Abstract: Input data for each of a plurality of stores is obtained. The plurality of stores are clustered into one or more department-level clusters based on the input data corresponding to at least one department value demographic for calculating a plurality of department space elasticity values. The plurality of stores are clustered into a plurality of store-level clusters based on a store level demographic. Ranking, by a space optimization module corresponding to at least one department, for each department-level cluster, to obtain a set of optimal departments for the space planning and optimization using a rapid linearization algorithm. Ranking the plurality of stores, for each department-level cluster, to obtain a set of optimal stores for the space planning and optimization. Generating for each of the set of optimal stores, by processing information associated with the set of optimal departments and the set of optimal stores using a nonlinear space optimization mechanism.

Abstract: Method and system for determining the total processing time required for executing a plurality of jobs (n). The plurality of jobs along with a mean processing time (?), and a queue length (k) are received. A number of preliminary jobs (p) of the plurality of jobs (n) are distributed across computing resources based upon the queue length (k). A first processing time (TF) required for the execution of the number of preliminary jobs (p) is determined. Further, a number of remaining jobs (n?p) of the plurality of jobs (n) are distributed for execution across the plurality of computing resources. A second processing time (TS) required for the execution of the set of remaining jobs (n?p) is computed. Finally, the total processing time is determined by summing TF and TS.

Abstract: Method and system for determining the total processing time required for executing a plurality of jobs. In certain aspects, a set of jobs along with a mean processing time and a queue length are received. The set of jobs are distributed across computing resources based upon the queue length. A set of priority jobs is received when the execution of a subset of jobs from the set of jobs has been executed in a first processing time (TF). Further, the set of priority jobs is distributed for execution across the plurality of computing resources. A second processing time (TS) required for the execution of the set of priority jobs is computed. Further, a third processing time (TT) required for the execution of the set of remaining jobs from the set of jobs is determined. Finally, the total processing time is determined by summing TF, TS, and TT.

Abstract: Method and system for determining the number of computing resources for executing a plurality of jobs. In accordance with the method and the system, the plurality of jobs along with a plurality of variables associated to the plurality of jobs may be received. The plurality of variables may include a mean processing time (?), a standard deviation (?), a service level agreement (?), and a confidence level (1-?). After the receipt of the plurality of variables, an inverse parameter (?) may be determined using the mean processing time (?), the standard deviation (?), and the confidence level (1-?). Further, a queue length (k) may be computed based upon the total processing time (T), the mean processing time (?), the standard deviation (?), and the inverse parameter (?). Finally, the queue length (k) and the number of jobs (n) may be processed in order to determine the number of computing resources.

Abstract: The present subject matter relates to systems and methods for assortment planning and optimization in a retail environment. In one implementation, a method for assortment planning and optimization is described. The method includes receiving assortment parameter data, and input information. The input information includes performance data, product data, fixture data and store data. Further, the method includes ranking product items based at least on the assortment parameter data and the input information. Furthermore, the method includes creating a listing of the product items based at least on the ranking. Such listing of the product items is processed based at least on predefined business rules, to generate one or more assortment solutions for providing optimal gross margins.