Abstract: A method and a system of cause analysis and correction for manufacturing data comprises: based on an plurality of historic manufacturing data, establishing abnormal classification rules and normal classification rules; comparing a current manufacturing data to the abnormal classification rules to identify a matching abnormal rule with the manufacturing data and an abnormal class thereof; comparing the current manufacturing data to the normal classification rules to determine a correcting rule and determine one or more correcting values of at least one of a plurality of manufacturing parameters; extracting abnormal features from the historic manufacturing data having the same condition as that of the manufacturing data, and extracting normal features from the historic manufacturing data matching the correcting rule; and based on the abnormal and the normal features, evaluating the cause contribution of the plurality of manufacturing parameters corresponding to the manufacturing data.

Abstract: A method for a system to generate an item recommendation. The method includes determining, for each of a plurality of roles, a plurality of reference utility values each for one of a plurality of items, and performing a normalization on the plurality of reference utility values determined for each of the plurality of roles. The method further includes determining a plurality of aggregate utility values each for one of the plurality of items, based on a plurality of normalized reference utility values determined for each of the plurality of roles, and determining one or more combinations of items for recommendation based on the plurality of aggregate utility values.

Abstract: A system and method for analyzing text stream message for a micro-blog are provided. The system includes a sliding window module, storing a plurality of text stream messages from the micro-blog and updating the plurality of text stream messages once every preset duration; a dynamic text weight module, receiving the plurality of text stream messages and calculating the plurality of text stream messages for generating a burst weight according to a dynamic text stream weight algorithm; a clustering module, clustering the plurality of text stream messages for generating a plurality of clusters by a clustering algorithm according to the plurality of text stream messages and the burst weight; and a memory device, storing the clusters.

Abstract: A method for developing multi-tenant application, a data accessing method of multi-tenant application and a system using the same are provided. The system includes a multi-tenant application manager, a storage unit, a business schema maintainer and a data entity maintainer. The multi-tenant application manager stores a multi-tenant application. The storage unit stores a metadata table and a data storage table. The business schema maintainer generates a table schema and a data accessing interface according to a business schema. The data entity maintainer performs a table updating operation on a tenant table schema in the metadata table of the storage unit according to the table schema. In addition, the multi-tenant application performs an accessing operation on the data storage table of the storage unit through the data accessing interface and the data entity maintainer.

Abstract: A method for monitoring and controlling a household appliance is provided. The method has the steps of: monitoring and sampling the power of the household appliance to obtain a first standby power range; monitoring and sampling the power of the household appliance to obtain a set of first real-time power data; calculating a first standby confidence level based on the number of times that the first real-time power data fall within the first standby power range; and determining that the household appliance is in a standby state if the first standby confidence level is greater than or equal to a standby confidence level threshold.

Abstract: A method for monitoring and controlling a household appliance is provided. The method has the steps of: monitoring and sampling the power of the household appliance to obtain a first standby power range; monitoring and sampling the power of the household appliance to obtain a set of first real-time power data; calculating a first standby confidence level based on the number of times that the first real-time power data fall within the first standby power range; and determining that the household appliance is in a standby state if the first standby confidence level is greater than or equal to a standby confidence level threshold.