Abstract: Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

Abstract: Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of predicted growth rates for a first set of customers of a product. Next, the system uses a set of features comprising the predicted growth rates to generate a set of customer segments for the product, wherein each customer segment in the set of customer segments includes a similar growth rate and a similar potential spending. For each customer segment in the set of customer segments, the system uses the similar growth rate and the similar potential spending to calculate a customer lifetime value (CLV) for the customer segment. Finally, the system outputs the CLV with a second set of customers assigned to the customer segment for use in managing sales activity with the second set of customers.

Abstract: In an example embodiment, a server accesses, for a plurality of customer accounts within a professional networking service, a plurality of features of the customer accounts stored with the professional networking service. The server accesses, for the plurality of customer accounts within the professional networking service, past sales records of products from the professional networking service to the plurality of employer accounts. The server determines, based on the past sales records and based on one or more features from the plurality of features, a product to upsell or cross-sell to a specific customer account. The server provides, as a digital transmission, indicia of the one or more features and the determined product.

Abstract: The present invention relates to a new type NLO crystal Strontium Beryllatoborate (Chemical formula Sr.sub.2 Be.sub.2 B.sub.2 O.sub.7, abbr. SBBO). The SBBO compound is synthesized by chemical reaction of solid state in high temperature sintering, Its chemical reaction equation is:2SrCO.sub.3 +2BeO+2H.sub.3 BO.sub.3 .fwdarw.Sr.sub.2 Be.sub.2 B.sub.2 O.sub.7 +2CO.sub.2 +3H.sub.2 OA flux method is applied to the growth of single crystal of Strontium Beryllatoborate, in which SrB.sub.2 O.sub.4, NaF and other fluorides were used as flux solvents. The result of measurement indicated that this compound has following structural and physical characteristics: space group: P6.sub.3 (C.sub.6.sup.6) point group: C .sub.6, unit cell: a=4.663(3) .ANG., c=15.311(7) .ANG.,z=2, V=283.3(.ANG.).sup.3.