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 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: A systematic explainer is described herein, which comprises local, model-agnostic, surrogate ML model-based explanation techniques that faithfully explain predictions from any machine learning classifier or regressor. The systematic explainer systematically generates local data samples around a given target data sample, which improves on exhaustive or random data sample generation algorithms. Specifically, using principles of locality and approximation of local decision boundaries, techniques described herein identify a hypersphere (or data sample neighborhood) over which to train the surrogate ML model such that the surrogate ML model produces valuable, high-quality information explaining data samples in the neighborhood of the target data sample.

Abstract: Disclosed herein are various approaches for sharing knowledge within and between organizations while protecting sensitive data. A machine learning model may be trained using training prompts querying a vector store to prevent unauthorized user disclosure of data derived from the vector store. A prompt may be received and a response to the prompt may be generated using the machine learning model based at least in part on the vector store.

Abstract: The present disclosure relates to LLM orchestration with vector store generation. An embeddings model may be selected to generate an embedding for a digital artifact. Metadata for the digital artifact may also be generated and stored in a vector store in association with the embedding. A user query may be received and categorized. One of a plurality of machine learning models may be selected based on the categorization of the user query. A prompt may be generated based at least in part on the user query, and the selected machine learning model may generate a response to the user query based at least in part on the prompt.

Abstract: Auto-parallel-load techniques are provided for automatically loading database objects from an on-disk database system into an in-memory database system. The auto-parallel-load techniques involve a pipeline that includes several components. In one implementation, each of the pipeline components is configured to receive, extract information from, and add information to, a “state object”. One or more of the pipeline components include logic that is based on the output of a corresponding machine learning model. The machine learning models used by the pipeline components may be trained from training sets from which outliers have been excluded, and may be used as the basis for generating linear models that are used during runtime, to produce estimates that affect the parameters of the auto-parallel-load operation.

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 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 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 implement a prediction-driven, rather than a trial-driven, approach to automatic data placement recommendations for partitioning data across multiple nodes in a database system. The system is configured to extract workload-specific features of a database workload running at a database system and dataset-specific features of a database running on the database system. The workload-specific features characterize utilization of the database workload. The dataset-specific features characterize how data is organized within the database. The system identifies a plurality of candidate keys for determining how to partition data stored in the database across nodes. Based at least in part on the workload-specific features, the dataset specific features, and the plurality of candidate keys, a set of candidate key combinations for partitioning data is generated.

Abstract: In an embodiment, a computer-implemented method includes receiving a query from a client and determining a query plan for the query. The query plan comprises one or more query operators for executing at least a portion of the query on a database. The method also includes sending the one or more query operators to one or more computing nodes for the one or more computing nodes to execute the one or more query operators on one or more data fragments of the database. In this example, each computing node of the one or more computing nodes hosts a respective data fragment of the one or more data fragments. Further, the method includes detecting an error in executing a first query operator by a first computing node on a first data fragment, and sending, in response to detecting the error, the first query operator to a replacement computing node for executing on the first data fragment hosted by the spare computing node.

Abstract: Systems, methods, and non-transitory computer-readable storage media are provided for predicting the likelihood or probability of a subscriber of a service to cancel or not renew a subscription. A method, according to one implementation, includes a step of receiving data pertaining to aspects of a service that is provided by a service provider to a subscriber in accordance with a subscription. The data may include one or more impact factors each having a positive, neutral, or negative influence on the likelihood of subscriber churn. The method also includes a step of using the one or more impact factors to predict the likelihood that the subscriber will cancel the subscription.

Abstract: Herein is resource-constrained feature enrichment for analysis of parse trees such as suspicious database queries. In an embodiment, a computer receives a parse tree that contains many tree nodes. Each tree node is associated with a respective production rule that was used to generate the tree node. Extracted from the parse tree are many sequences of production rules having respective sequence lengths that satisfy a length constraint that accepts at least one fixed length that is greater than two. Each extracted sequence of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies that same length constraint. Based on the extracted sequences of production rules, a machine learning model generates an inference. In a bag of rules data structure, the extracted sequences of production rules are aggregated by distinct sequence and duplicates are counted.

Abstract: A method, system, and computer program product for false decline mitigation. The method includes obtaining an objective function associated with an issuer system; training a neural network, based on prior transaction data associated with one or more prior transactions, to optimize the objective function; providing the trained neural network; receiving transaction data generated, based on one or more case creation (CC) rules, during processing of a transaction associated with an account identifier; processing, using the trained neural network, the transaction data to generate an exclude account list including the account identifier; receiving subsequent transaction data associated with a subsequent transaction associated with the account identifier; and authorizing, based on the exclude account list and the account identifier, the subsequent transaction associated with the account identifier without applying one or more real-time decisioning (RTD) rules to the subsequent transaction.

Abstract: Techniques are described herein for performing efficient matrix multiplication in architectures with scratchpad memories or associative caches using asymmetric allocation of space for the different matrices. The system receives a left matrix and a right matrix. In an embodiment, the system allocates, in a scratchpad memory, asymmetric memory space for tiles for each of the two matrices as well as a dot product matrix. The system proceeds with then performing dot product matrix multiplication involving the tiles of the left and the right matrices, storing resulting dot product values in corresponding allocated dot product matrix tiles. The system then proceeds to write the stored dot product values from the scratchpad memory into main memory.

Abstract: Disclosed is an improved approach to implement anomaly detection, where an ensemble detection mechanism is provided. An improvement is provided for the KNN algorithm where scaling is applied to permit efficient detection of multiple categories of anomalies. Further extensions are used to optimize local anomaly detection.

Abstract: Herein is resource-constrained feature enrichment for analysis of parse trees such as suspicious database queries. In an embodiment, a computer receives a parse tree that contains many tree nodes. Each tree node is associated with a respective production rule that was used to generate the tree node. Extracted from the parse tree are many sequences of production rules having respective sequence lengths that satisfy a length constraint that accepts at least one fixed length that is greater than two. Each extracted sequence of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies that same length constraint. Based on the extracted sequences of production rules, a machine learning model generates an inference. In a bag of rules data structure, the extracted sequences of production rules are aggregated by distinct sequence and duplicates are counted.

Abstract: Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.

Abstract: Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS.

Abstract: The present disclosure relates to methods, systems, and apparatuses for providing electronic communications to client devices based on clustering and filtering candidates for inclusion in the electronic communications based on programmatically generated correlation metrics and thresholds associated with the clustering.