International Business Machines Corporation

Miao He

Bing Shao

Changrui Ren

Yue Tong

Arun Hampapur

Xuan Liu

Ajay Ashok Deshpande

Christopher Scott Milite

Jae-Eun Park

Yingjie Li

Ming Xie

Hongliang Fei

Alan Jonathan King

Xiao Mei Zheng

Songhua Xing

Kimberly D Hendrix

Chang Rui Ren

JoAnn Piersa Brereton

Vadiraja S Ramamurthy

Steve Igrejas

Joline Ann Villaranda Uichanco

Wei Wang

Jian Xu

Feng Chen

Qin Hua Wang

Lin Tang

Ming Wan

Tian Zhi Zhao

Xu Zhang

Jin Feng Li

An example operation may include one or more of identifying pending blockchain transactions in a transaction queue, determining states of the pending blockchain transactions, determining whether the pending blockchain transactions in the transaction queue are valid based on the determined states, retrieving a list of potential blockchain transaction conflicts associated with the pending blockchain transactions, and determining whether any conflicts exist for one or more of the pending blockchain transactions based on the list of blockchain transaction conflicts.

Parallel transaction validation and block generation in a blockchain

A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties. One example may comprise one or more of creating one or more control commands configured to control one or more smart devices, signing the one or more control commands via a key maintained by an entity creating the control commands, broadcasting the one or more control commands to the one or more smart devices, and storing the one or more control commands in a blockchain.

Monitoring actions performed by a network of peer devices using a blockchain

Machine logic (for example, software) for protecting sensitive and confidential user data and sending the protected user data to an authorized third party for predictive analytics purposes. The machine logic rules perform linear transformation operations on the sensitive and confidential user data in order to obscure the underlying data before it is sent to an authorized third party for performing predictive analysis of the underlying data. After performing predictive analysis on the obscured data by the authorized third party, the owner of the sensitive and confidential data takes a consequential action (that is, the owner of the data makes a business-related decision) based on the prediction.

Protection of data privacy for cognitive demand forecasting

A computer-implemented data transmission method and system are provided. A first transformed password (PWD) is acquired from the first node, wherein the first transformed PWD is derived from an original PWD. An encrypted message is acquired from the first node, the encrypted message being encrypted with a public key of the first node. A private key of the first node is recovered according to the first transformed PWD, the private key having been encrypted with the first transformed PWD and stored in the second node in advance. The encrypted message is decrypted with the recovered private key to obtain a decrypted message for processing.

Sending message in multilayer system

A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Big data sourcing simulator

A mechanism is provided for identifying suggestions as to which log files associated with data in a data warehouse to search for particular data. Features from the contents of a plurality of log files from a set of log files are selected. The plurality of log files are grouped based on the selected features. Using extracted log event sequences, a correlation between the log event sequences and a plurality of data transaction tables from a set of data transaction tables in the data warehouse is calculated. Suggestions as to which log files in the set of log files should be searched is then identified for particular data based on a business relevance value and a utilized data ratio.

Generating important values from a variety of server log files

Technical solutions are described for improving a data-mining process for identifying a desired population of a dataset. An example computer-implemented method includes, receiving the dataset that includes a plurality of data dimensions. The method also includes determining a set of predictors for a target variable, where a predictor is a data dimension that is relevant to the target variable. The method also includes identifying from the set of predictors, a false predictor, where the false predictor is a data dimension that results in an empty set of the desired population. The method also includes removing the false predictor from the set of predictors used for the data-mining process for identifying the desired population of the dataset.

Eliminating false predictors in data-mining

A method for generating cube metadata based on an enhanced star schema may be provided. In some embodiments, the method may include extracting dimension references from a factless fact table in an enhanced star schema. The enhanced star schema may include a fact table, a plurality of dimension tables of the fact table and the factless fact table. In some embodiments, a hierarchy reference may be constructed based on the dimension references. In some embodiments, cube metadata may be generated by combining the hierarchy reference with measures obtained from the fact table and a hierarchy obtained from the dimension tables in the enhanced star schema.

Generation of cube metadata and query statement based on an enhanced star schema

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Miao He