Abstract: In some embodiments, a computer system generates offline a viewer embedding for a user of an online service based on a viewer portion of a scoring model using viewer features stored in a data source in association with a profile of the user, generates offline candidate embeddings for recommendation candidates by a key-value store based on a recommendation portion of the scoring model using features of the recommendation candidate stored in the key-value store, pushes the viewer embedding to the key-value store, generates online pairwise scores for recommendation candidates by the key-value store based on a pairwise portion of the scoring model using the viewer embedding and candidate embeddings, generates ranking scores for the recommendation candidates based on the scoring model using the embeddings and the pairwise scores, and causes recommendation candidates to be displayed on a device of the user based on the corresponding ranking scores.

Abstract: Computer-implemented techniques for lossless and lossy summarization of large-scale graphs. Beneficially, the lossless summarization process is designed such that it can be performed in a parallel processing manner. In addition, the lossless summarization process is designed such that it can be performed with having to store only a certain small number of adjacency list node objects in-memory at once and without having to store an adjacency list representation of the entire input graph in-memory at once. In some embodiments, the techniques involve further summarizing the reduced graph output from the lossless summarization process in a lossy manner. Beneficially, the lossy summarization process uses a condition that is computationally efficient to evaluate when determining whether to drop edges of the reduced graph while at the same time ensuring the accuracy of a graph restored from the lossy reduced graph compared to the input graph is within the error bound.

Abstract: A system, method and computer program product for obtaining a traffic route for a vehicle. The system receives information of a current location of the vehicle. The system determines a destination location or destination region of the vehicle. The system computes a plurality of available traffic routes from the current location to the destination location or to the destination region. The system estimates a potential demand for at least one potential passenger from the current location to the destination location or to the destination region per each available traffic route. The system recommends, to a driver of the vehicle, an available traffic route that has a highest estimated potential demand among a plurality of the available traffic routes.

Abstract: Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Abstract: An apparatus hosting a multi-tenant software-as-a-service (SaaS) system maximizes resource sharing capability of the SaaS system. The apparatus receives service requests from multiple users belonging to different tenants of the multi-tenant SaaS system. The apparatus partitions the resources in the SaaS system into different resource groups. Each resource group handles a category of the service requests. The apparatus estimates costs of the service requests of the users. The apparatus dispatches service requests to resource groups according to the estimated costs, whereby the resources are shared, among the users, without impacting each other.

Abstract: A method, system and computer program product for detecting outliers in a set of data points. In one embodiment, the method comprises partitioning the set of data points into a plurality of bins with each of the data points assigned to a respective one of the bins. A plurality of local lists are formed in parallel identifying points in the bins as outliers, and the local lists are merged into a global list to identify one or more of the points as outliers of the data set. Embodiments of the invention provide an outlier detection system that can parallelize in two levels. The dataset is split into partitions, called bins, and outliers are found in each bin in parallel. The execution of a single bin is also parallelized. Embodiments of the invention can scale to very large datasets by these two modes of parallelism.

Abstract: Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Abstract: A system, method and computer program product for obtaining a traffic route for a vehicle. The system receives information of a current location of the vehicle. The system determines a destination location or destination region of the vehicle. The system computes a plurality of available traffic routes from the current location to the destination location or to the destination region. The system estimates a potential demand for at least one potential passenger from the current location to the destination location or to the destination region per each available traffic route. The system recommends, to a driver of the vehicle, an available traffic route that has a highest estimated potential demand among a plurality of the available traffic routes.

Abstract: An apparatus hosting a multi-tenant software-as-a-service (SaaS) system maximizes resource sharing capability of the SaaS system. The apparatus receives service requests from multiple users belonging to different tenants of the multi-tenant SaaS system. The apparatus partitions the resources in the SaaS system into different resource groups. Each resource group handles a category of the service requests. The apparatus estimates costs of the service requests of the users. The apparatus dispatches service requests to resource groups according to the estimated costs, whereby the resources are shared, among the users, without impacting each other.

Abstract: A method and an apparatus to improve processor utilization in data mining have been disclosed. In one embodiment, the method includes representing a transaction data set with a prefix tree, and allocating the prefix tree in a depth first search order in a memory of the computing system during data mining of the transaction data set. Other embodiments have been claimed and described.