Abstract: Techniques are described herein for indexing personal information in columnar data storage format based files. In an embodiment, row groups of rows that comprise a plurality of columns are stored in a set of files. Each column of a row group is stored in a chunk of column pages in the set of files. A regular expression index that indexes a particular column in the set of files is stored for each row group. The regular expression index identifies column pages in the chunk of the particular column that include a particular column value that satisfies a regular expression specified in a query. The regular expression specified in the query in evaluated against the particular column using the regular expression index.

Abstract: Herein are techniques for automatic tuning of hyperparameters of machine learning algorithms. System throughput is maximized by horizontally scaling and asynchronously dispatching the configuration, training, and testing of an algorithm. In an embodiment, a computer stores a best cost achieved by executing a target model based on best values of the target algorithm's hyperparameters. The best values and their cost are updated by epochs that asynchronously execute. Each epoch has asynchronous costing tasks that explore a distinct hyperparameter. Each costing task has a sample of exploratory values that differs from the best values along the distinct hyperparameter. The asynchronous costing tasks of a same epoch have different values for the distinct hyperparameter, which accomplishes an exploration. In an embodiment, an excessive update of best values or best cost creates a major epoch for exploration in a subspace that is more or less unrelated to other epochs, thereby avoiding local optima.

Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.

Abstract: A partial group by operator is a group by operator that implements a fallback mechanism. The fallback mechanism is triggered whenever memory pressure reaches a certain threshold. When the fallback mechanism is triggered, a row is included in an output of the partial group by operator without including an aggregation value for a grouping value for the row to an aggregation data structure. A final group by operator computes a final aggregate value of all results, including pre-grouped results and passed through results, from the partial group by operator.

Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automate database configuration parameter tuning for a database workload. This approach uses machine learning (ML) models to test performance metrics resulting from application of particular database parameters to a database workload, and does not require live trials on the DBMS managing the workload. Specifically, automatic configuration (AC) ML models are trained, using a training corpus that includes information from workloads being run by DBMSs, to predict performance metrics based on workload features and configuration parameter values. The trained AC-ML models predict performance metrics resulting from applying particular configuration parameter values to a given database workload being automatically tuned. Based on correlating changes to configuration parameter values with changes in predicted performance metrics, an optimization algorithm is used to converge to an optimal set of configuration parameters.

Abstract: Techniques are described herein for influencing plan generation in context of the two phase query optimization approach. Types of pruning criteria including method pruning criteria, total cost pruning criteria, and permutation pruning criteria exist in cost-based plan generators to determine what parts of a query statement should be offloaded to a query offload engine. Method pruning criteria is responsible to determine an optimal joining method. Total cost pruning criteria compares accumulated costs with a lowest plan cost determined so far. Permutation pruning criteria is responsible for selecting the cheapest query execution plan from all considered query execution plans. Each type of pruning criteria is modified to favor offload engine execution upon request.

Abstract: System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for a condition of each of the plurality of Wi-Fi networks; determining one or more resolutions for the condition of a Wi-Fi network of the plurality of Wi-Fi networks; and initiating a customer outreach workflow to provide the one or more resolutions to the customer associated with the Wi-Fi network.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for Wi-Fi metric based alarms, each Wi-Fi metric based alarm being associated with detection of one of an offline Wi-Fi network of the plurality of Wi-Fi networks, an offline node of the Wi-Fi network, instability of the Wi-Fi network, congestion in the Wi-Fi network, and interference in the Wi-Fi network; determining the Wi-Fi metric based alarms based on the analyzing; and performing one or more actions based on the determined Wi-Fi metric based alarms.

Abstract: Embodiments monitor statistics from groups of devices and generate an alarm upon detecting a utilization imbalance that is beyond a threshold. Particular balance statistics are periodically sampled, over a timeframe, for a group of devices configured to have balanced utilization. The devices are ranked at every data collection timestamp based on the gathered device statistics. The numbers of times each device appears within each rank over the timeframe are tallied. The device/rank summations are collectively used as a probability distribution representing the probability of each device being ranked at each of the rankings in the future. Based on this probability distribution, an entropy value that represents a summary of the imbalance of the group of devices over the timeframe is derived. An imbalance alert is generated when one or more entropy values for a group of devices shows an imbalanced utilization of the devices going beyond an identified imbalance threshold.

Abstract: Techniques described herein propose a ROWID Elimination Rewrite that uses functional dependencies to substitute and/or eliminate ROWID pseudo-columns referenced in a query in order to reduce memory pressure and speed up processing.

Abstract: Techniques related to a sparse dictionary tree are disclosed. In some embodiments, computing device(s) execute instructions, which are stored on non-transitory storage media, for performing a method. The method comprises storing an encoding dictionary as a token-ordered tree comprising a first node and a second node, which are adjacent nodes. The token-ordered tree maps ordered tokens to ordered codes. The ordered tokens include a first token and a second token. The ordered codes include a first code and a second code, which are non-consecutive codes. The first node maps the first token to the first code. The second node maps the second token to the second code. The encoding dictionary is updated based on inserting a third node between the first node and the second node. The third node maps a third token to a third code that is greater than the first code and less than the second code.

Abstract: Techniques are provided for scheduling data operations for a given query based upon a query-cost model that analyzes the cost of scheduling data operations based upon their operation cost and the type of resources needed for the operation. In an embodiment, a database server receives a set of operations for a query. The database server determines a set of leaf operation nodes from the set of data operations, where the set of leaf operation nodes includes operation nodes that do not depend on the execution of other nodes within the set of data operations. The database server compares operation costs between the leaf operation nodes to determine which leaf operation node to insert into a scheduled order set. The database server inserts the leaf operation node into the scheduled order set. Then the database server iteratively determines new leaf operation nodes and performs cost analysis on remaining leaf operation nodes to generate a set of scheduled data operations.

Abstract: A method, apparatus, and computer program product are disclosed for self-service design, scheduling, and delivery of user-defined reports regarding promotions. The method includes receiving, from a user device, a report type and report delivery information. Based on the report type, relevant data regarding the one or more promotions is collected, using which a report is generated. The method then outputs the generated report based on the report delivery information. Optionally, analytical insights, such as trends within the data, sample size, suitability of control data, and indications of statistical significance, are generated and included in the report. A corresponding apparatus and computer program product are also provided.

Abstract: The present disclosure relates to methods, systems, and apparatuses for providing promotion recommendations using a promotion and marketing service. Some aspects may provide a method for providing a promotion recommendation framework. The method includes receiving, via a network interface, a promotion recommendation inquiry from a component of a promotion and marketing service, the promotion recommendation inquiry including electronic identification data identifying at least one of a consumer or a consumer characteristic. The method also includes identifying, via processing circuitry, promotion transaction information associated with the electronic identification data. The promotion transaction information includes electronic data identifying at least one transaction performed using the promotion and marketing service.

Abstract: Embodiments use Bayesian techniques to efficiently estimate the bit error rates (BERs) of cables in a computer network at a customizable level of confidence. Specifically, a plurality of probability records are maintained for a given cable in a computer system, where each probability record is associated with a hypothetical BER for the cable, and reflects a probability that the cable has the associated hypothetical BER. At configurable time intervals, the probability records are updated using statistics gathered from a switch port connected to the cable. In order to estimate the BER of the cable at a given confidence level, embodiments determine which probability record is associated with a probability mass that indicates the confidence level. The estimate for the cable BER is the hypothetical BER that is associated with the indicated probability mass. Embodiments store the estimate in memory and utilize the estimate to aid in maintaining the computer system.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data based on one or more use cases; determining any of predictions and alerts for the one or more use cases based on the analyzing; and performing one or more actions based on the any of predictions and alerts.

Abstract: Techniques are described herein for influencing plan generation in context of the two phase query optimization approach. Types of pruning criteria including method pruning criteria, total cost pruning criteria, and permutation pruning criteria exist in cost-based plan generators to determine what parts of a query statement should be offloaded to a query offload engine. Method pruning criteria is responsible to determine an optimal joining method. Total cost pruning criteria compares accumulated costs with a lowest plan cost determined so far. Permutation pruning criteria is responsible for selecting the cheapest query execution plan from all considered query execution plans. Each type of pruning criteria is modified to favor offload engine execution upon request.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for recommending contextually relevant promotions to consumers in order to facilitate their discovery of promotions that they are likely to purchase from a promotion and marketing service.

Abstract: Techniques herein partition data using data repartitioning that is store-and-forward, content-based, and phasic. In embodiments, computer(s) maps network elements (NEs) to grid points (GPs) in a multidimensional hyperrectangle. Each NE contains data items (DIs). For each particular dimension (PD) of the hyperrectangle the computers perform, for each particular NE (PNE), various activities including: determining a linear subset (LS) of NEs that are mapped to GPs in the hyperrectangle at a same position as the GP of the PNE along all dimensions of the hyperrectangle except the PD, and data repartitioning that includes, for each DI of the PNE, the following activities. The PNE determines a bit sequence based on the DI. The PNE selects, based on the PD, a bit subset of the bit sequence. The PNE selects, based on the bit subset, a receiving NE of the LS. The PNE sends the DI to the receiving NE.