Abstract: Techniques are described for partitioning and rebalancing data storage, such as through management of database partitions. In one or more implementations, a database that includes existing partitions is repartitioned to include new partitions. A balancing function that uses a skew factor is implemented that skews new data allocation to the new partitions. In at least some implementations, the skew factor can be removed from new data allocation, such as in response to an indication that data allocation between the new partitions and the existing partitions is unbalanced.

Abstract: Techniques are described for partitioning and rebalancing data storage, such as through management of database partitions. In one or more implementations, a database that includes existing partitions is repartitioned to include new partitions. A balancing function that uses a skew factor is implemented that skews new data allocation to the new partitions. In at least some implementations, the skew factor can be removed from new data allocation, such as in response to an indication that data allocation between the new partitions and the existing partitions is unbalanced.

Abstract: Composite partition function techniques are described herein that may be used to repartition a database without moving existing data. In one or more implementations, a database is partitioned in a first arrangement associated with a first partition function. The database is repartitioned to form a second arrangement associated with a second partition function. A record of key membership for data entries in the partitions existing at the time of repartitioning is created, which can be used to recognize data corresponding to the first arrangement. A composite partition function is defined for subsequent database operations that is configured to apply the first partition function for data having keys found in the record of key membership and apply the second partition function for other data having keys that are not found in the record of key membership.

Abstract: Systems, methods, and software for operational anomaly detection in communication systems is provided herein. An exemplary method includes obtaining a measured sequence of state information associated with the communications system during a first timeframe, processing the measured sequence of state information to determine a predicted sequence of state information for the communication system during a second timeframe, and monitoring current state information for the communication system over at least a portion of the second timeframe. The method also includes determining operational anomalies associated with the communication system based at least on a comparison between the current state information and the predicted sequence of state information.

Abstract: Training data is collected describing multiple past messages sent over a computer-implemented communication service. For each of the past messages, the training data set comprises a record of a respective channel of the respective message, and a record of respective feature vector of the respective message, wherein the channel corresponds to a respective one or more recipients to which the respective message was sent, and wherein the feature vector comprises a respective set of values of a plurality of parameters associated with the sending of the respective message. The training data is used to train a machine learning algorithm. By applying the machine learning algorithm to the feature vector of a respective subsequent message, to be sent by a sending user over the computer-implemented communication service, a prediction is generated regarding one or more potential recipients of the subsequent message.

Abstract: Training data is collected describing multiple past communications over a computer-implemented communication service. For each of the past communications, the training data set comprises a record of a respective recipient of the respective communication, and a record of respective feature vector of the respective communication, wherein the recipient is defined in terms of an identity of in individual person, and wherein the feature vector comprises a respective set of values of a plurality of parameters associated with the respective communication. The training data is used to train a machine learning algorithm. By applying the machine learning algorithm to the feature vector of a respective subsequent message, to be sent by a sending user over the computer-implemented communication service, a prediction is generated regarding one or more potential recipients of the subsequent message.

Abstract: Composite partition function techniques are described herein that may be used to repartition a database without moving existing data. In one or more implementations, a database is partitioned in a first arrangement associated with a first partition function. The database is repartitioned to form a second arrangement associated with a second partition function. A record of key membership for data entries in the partitions existing at the time of repartitioning is created, which can be used to recognize data corresponding to the first arrangement. A composite partition function is defined for subsequent database operations that is configured to apply the first partition function for data having keys found in the record of key membership and apply the second partition function for other data having keys that are not found in the record of key membership.

Abstract: Various embodiments enable a system, such as an audio conferencing system, to remove voices from an audio conference in which the removed voices are not desired. In at least some embodiments, an audio signal associated with the audio conference is analyzed and components which represent the individual voices within the audio conference are identified. Once the audio signal is processed in this manner to identify the individual voice components, a control element can be applied to filter out one or more of the individual components that correspond to undesired voices.

Abstract: Various embodiments enable web clients to provide web services. In one or more embodiments, web clients are configured to act as servers and can be used for, among other purposes, to and from calling of web services supported by the web client. Such uses can include, by way of example and not limitation, pushing messages to and from the web client. The various techniques can utilize standard protocols and libraries and without the need for any custom code or custom plug-ins.