International Business Machines Corporation

Junmei Qu

Liang Liu

Wei Zhuang

Li Li

Wen Jun Yin

Lingyun Wang

Hong Zhou Sha

Xin Xin Bai

Chao Zhu

Jin Yan Shao

Xi Xia

ChaoQiang Zhu

Jin Dong

Yi Ge

Yu Fen Yuan

Zhuo Liu

Chao Zhang

Fan Jing Meng

Jia Qi Li

Ya Ming Zhang

Feng Jin

Qi Ming Tian

Xiao Guang Rui

Pei Ni Liu

Jian Dong Ding

Zi Xiao Zhu

Chun Hai Chen

Tao Tong

Yuhang Liu

A method and system for operation objects discovery from operation data includes performing pattern matching of operation data with patterns in a database. Fields in the operation data are identified as having matching patterns with the database as first potential objects. Data profiling is performed on unmatched fields of the operation data to generate data profiles. The data profiles are field classified and second potential objects are generated. The first potential objects and the second potential objects are de-duplicated, and operation objects are generated.

Method and system for operation objects discovery from operation data

A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

Abnormal air pollution emission prediction

This disclosure provides a computer-implemented method. The method may comprise estimating a value range of a water body parameter based on measured data for a water quality indicator of a first set of time-spatial points and measured data for the water quality indicator of a second set of time-spatial points; and determining an optimal value of the water body parameter from the estimated value range by comparing the measured data for the water quality indicator of the second set and simulated data for the water quality indicator of the second set, wherein the simulated data for the water quality indicator of the second set is obtained based on a fluid dynamic model using the measured data for the water quality indicator of the first set as an input of the fluid dynamic model and using a value in the estimated value range as a parameter of the fluid dynamic model.

Water quality prediction

An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Detection of area of abnormal air quality within a geographical area

An input data set for a model for estimating a missing value of a sensor in a sensor network is determined. The input data set includes one or more sensor readings selected according to a temporal-spatial parameter which is dynamic and specific to the sensor. Then the missing value of the sensor is estimated using the determined input data set as an input to the model.

Missing sensor value estimation

Correction management techniques are provided. In one embodiment, the techniques involve determining, via a first machine learning model, a first and second data based on a respective first and second raw data obtained from a plurality of sensors, determining, based on a deviation between the first data and the second data, an inaccuracy of the first data, identifying an ambient situation corresponding to the first raw data and the second raw data, selecting, from historical raw data of the plurality of sensors, a group of raw data corresponding to the ambient situation, and correcting, via a second machine learning model, the first data based on the selected group of raw data.

Error correction

In one embodiment, a method for managing a data storage system includes: in response to receiving a data object, sorting data records in the data object on the basis of a first query so as to form a first backup; causing the first backup to be stored in the data storage system; and cause to be stored, in an index of the data storage system, the first query and a first address of the first backup in the data storage system.

Managing data storage system

A method, system, and computer program product, include identifying a plurality of pollution process sets and determining pollution sources based on pollution start times of target pollution processes with matched features in the plurality of pollution process sets within a time window.

System, method and computer program product for pollution source attribution

Disclosed is a novel system, computer program product, and method to compute an air quality forecast. An air quality forecast model, air quality real-time monitoring data, and air quality forecast data is accessed. A deviation in air pollution emission is monitored by classifying a difference between the air quality monitoring data and the air quality forecast data. This monitoring includes classifying any weather differences which are caused by weather, classifying any terrain differences which are caused by a geographic terrain; and, filtering the difference caused by inaccurate pollution emission inventory. The monitoring may repeat in response to a given time period elapsing or a chance in air quality forecast data received.

Air quality forecast by adapting pollutant emission inventory

Embodiments of the present disclosure relates to data block processing in a distributed processing system. According to one embodiment of the present disclosure, a computer-implemented method is proposed. A first performance indicator for processing a data block by a first processing module is obtained, where the data block is loaded into the first processing module. Then, a second performance indicator for processing the data block by a second processing module is obtained, where the first and second processing modules being logical instances launched in a distributed processing system for processing data blocks. Next, one processing module is selected from the first and second processing modules for processing the data block based on a relationship between the first and second performance indicators.

Data block processing

Aspects of the present invention include a method, which includes updating, by a processor, one or more distributed memory datasets having data stored therein in response to a write data operation, the one or more distributed data memory datasets being located in a database. The method further includes splitting, by the processor, any one of the one or more distributed memory datasets into two distributed memory datasets when a size of the any one of the one or more distributed memory datasets exceeds a threshold value. The method further includes moving, by the processor, the stored data in any one of the one or more distributed memory datasets to regions within the database upon an occurrence of one or more conditions with respect to the one or more distributed memory datasets. Other aspects of the present invention include a system and a computer program product.

Data write/import performance in a database through distributed memory

Technical solutions are described for optimizing operation of a server cluster. An example method includes receiving a job request that executes using a set of data blocks, the job request being associated with an expected completion time. The cluster server is used to identify a set of replica servers, wherein each server from the set of replica servers contains the set of data blocks. In response to each server from the set of replica servers estimating a completion time for the job request that is more than the expected completion time, a new server is initiated, the set of data blocks is relocated from a first server from the set of replica servers to the new server, and the job request is allocated to the new server.

Data and task reallocation in distributed computing platforms

A method, system, and computer program product, include determining a task resource consumption predicted for each of one or more tasks being executed on a node, wherein the task resource consumption is a function of time and predicting a node resource consumption of the node based at least on the predicted task resource consumption, wherein the node resource consumption is a function of time.

System, method and computer program product for resource management in a distributed computation system

A mechanism is provided for detecting air-pollution anomalies. A historical air-pollution pattern is identified for each of a plurality of air-pollution monitoring stations. For each of the plurality of air-pollution monitoring stations, responsive to receiving real-time data from a particular air-pollution monitoring station, the real-time data is compared to the historical air-pollution pattern associated with the particular air-pollution monitoring station. A density difference value is generated based on the comparison of the real-time data to the historical air-pollution pattern associated with the particular air-pollution monitoring station and a determination is made as to whether the density difference value is greater than a predetermined confidence threshold. For each of a subset of the plurality of air-pollution monitoring stations whose associated density difference value is greater than the predetermined confidence threshold, an indication of an anomaly in detected air-pollution levels is identified to an administrator.

Air-pollution anomaly location mechanism

A method, system, and computer program product, include determining a first region based on a first point, a second point and a third point associated with temperature indication information that represents association relationship between temperature and pressure, the first and second points being associated with a same temperature value and different pressure values, a temperature inversion ending at the first point, and the temperature inversion starting from the third point, determining, within the first region, a second region associated with the temperature inversion, and predicting atmospheric condition based on the first and second regions.

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Beijing, China

Junmei Qu