Abstract: In accordance with an embodiment, described herein is a system and method for transaction recovery in a multitenant application server environment. At least one resource manager associated with a partition can be designated as a determiner resource for that partition only, in order to support eliminating transaction logs (TLOG) in processing a two-phase commit transaction. A transaction manager can prepare all other resource managers in the mid-tier transactional system before the determiner resource. Furthermore, the transaction manager can rely on the list of outstanding transactions to be committed that is provided by the determiner resource for recovering the transaction. The transaction manager can commit an in-doubt transaction returned from a resource manager that matches the list of in-doubt transactions returned from the determiner resource. Otherwise, the transaction manager can roll back the in-doubt transaction.

Abstract: Techniques for a database management system to predict when in the future a materialized view will be used for query rewrite. This is a followed by an approach that uses the quiet rewrite pattern prediction to determine an optimized schedule for refreshing the materialized view. The approach combines the query rewrite pattern prediction with a quiet period prediction for the materialized view and an estimated refresh duration for the materialized view to determine the optimized refresh schedule for the materialized view.

Abstract: Techniques for automatically switching between chatbot skills in the same domain. In one particular aspect, a method is provided that includes receiving an utterance from a user within a chatbot session, where a current skill context is a first skill and a current group context is a first group, inputting the utterance into a candidate skills model for the first group, obtaining, using the candidate skills model, a ranking of skills within the first group, determining, based on the ranking of skills, a second skill is a highest ranked skill, changing the current skill context of the chatbot session to the second skill, inputting the utterance into a candidate flows model for the second skill, obtaining, using the candidate flows model, a ranking of intents within the second skill that match the utterance, and determining, based on the ranking of intents, an intent that is a highest ranked intent.

Abstract: Obtaining collections of sentences in different languages that are usable for training models in various applications of artificial intelligence is provided. A method is provided that obtains, from text corpus, webpages in a plurality of languages, each of the webpages corresponding to an URL; obtains annotations for each of the webpages based on its URL, to obtain annotated data entries corresponding to the webpages, each of the annotated data entries including a classification label corresponding to a sub-topic of one of a plurality of topics, where each of the plurality of topics includes a corresponding plurality of sub-topics; filters the annotated data entries to obtain topic-specific content in a target language based on the classification labels, the topic-specific content corresponding to one or more sub-topics; performs post-processing on the topic-specific content to obtain result data; and outputs the result data for the topic.

Abstract: Techniques are described to determine whether an input utterance is unrelated to a set of skill bots associated with a master bot. In some embodiments, a system described herein includes a training system and a master bot. The training system trains a classifier of the master bot. The training includes accessing training utterances associated with the skill bots and generating training feature vectors from the training utterances. The training further includes generating multiple set representations of the training feature vectors, where each set representation corresponds to a subset of the training feature vectors, and configuring the classifier with the set representations. The master bot accesses an input utterance and generates an input feature vector. The master bot uses the classifier to compare the input feature vector to the multiple set representations so as to determine whether the input feature falls outside and, thus, cannot be handled by the skill bots.

Abstract: Techniques are described herein for achieving physically dispersed item-dispersal patterns in queue tables that are used as circular buffers in a FIFO system. The techniques achieve the physically dispersed item-dispersal patterns while retaining FIFO semantics, efficient data ingest, efficient data access, and low latency for both fixed and variable-size ingest data. Because consecutively inserted rows are written to physically dispersed locations on persistent storage, lock contention between concurrent ingest sessions is significantly reduced. Once a physically dispersed item-dispersal pattern is established for a first set of inserted rows, subsequent rows are added by overwriting the existing rows. The physical dispersal of the initial rows may be accomplished by setting values in a slot number column such that updates that are made in a sequence that is based on the slot number column exhibit a desired physical dispersal pattern.

Abstract: A sharded, permissioned, distributed ledger may reduce the amount of work and communication required by each participant, thus possibly avoiding scalability bottlenecks that may be inherent in previous distributed ledger implementations and possibly enabling the use of additional resources to translate to increased throughput. A sharded, permissioned, distributed ledger may be made up of multiple shards, each of which may also be a distributed ledger and which may operate in parallel. Participation within a sharded, permissioned, distributed ledger may be allowed only with permission of an authority. A sharded, permissioned, distributed ledger may include a plurality of nodes, each including a dispatcher configured to receive transaction requests from clients and to forward received requests to verifiers configured to append transactions to individual ones of the shards.

Abstract: The present invention relates to optimized access of a database. Herein are techniques to accelerate execution of any combination of ad hoc query, heterogenous hardware, and fluctuating workload. In an embodiment, a computer receives a data access request for data tuples and compiles the data access request into relational operators. A particular implementation of a particular relational operator is dynamically selected from multiple interchangeable implementations. Each interchangeable implementation contains respective physical operators. A particular hardware operator for a particular physical operator is selected from multiple interchangeable hardware operators that include: a first hardware operator that executes on first processing hardware, and a second hardware operator that executes on second processing hardware that is functionally different from the first processing hardware.

Abstract: The present embodiments relate to a secure boot partition for a cloud computing device of a cloud computing system. The computing device of the cloud computing system can transmit a first request for a pre-boot execution environment executable from a smart network interface card (SmartNIC). The computing device can receive the pre-boot environment executable from the SmartNIC and verify the pre-boot execution environment executable. The computing device can execute the pre-boot execution environment executable. Executing the pre-boot execution environment executable can include transmitting a second request secure boot metadata from the SmartNIC and receiving the secure boot metadata. Executing the pre-boot execution environment executable can further include mounting a boot partition, loading a boot loader obtained from the boot partition, verifying the boot loader based at least in part on the secure boot metadata, and executing the boot loader in response to verifying the boot loader.

Abstract: A process for defining a cloud service in an Infrastructure as Code (IaC) tool is designed to dynamically determine numbers of clusters, availability domains, compute nodes, and/or load balancers at run time. These values are then used to determine a number of subnet levels required for a classless inter-domain routing (CIDR) slicing operation to generate subnets for each of the compute nodes and load balancer nodes in the service. Because IaC languages do not provide nested loop control constructs, labels for each of the subnets can be generated and assigned using a Cartesian product of label elements. These labels can be modified by a count variable that is incremented each time a resource is duplicated in the script to simulate the effect of a nested loop structure.

Abstract: A computing device may receive a set of user documents. Data may be extracted from the documents to generate a first graph data structure with one or more initial graphs containing key-value pairs. A model may be trained on the first graph data structure to classify the pairs. Until a set of evaluation metrics for the model exceeds a set of deployment thresholds: generating, a set of evaluation metrics may be generated for the model. The set of evaluation metrics may be compared to the set of deployment thresholds. In response to a determination that the set of evaluation metrics are below the set of deployment thresholds: one or more new graphs may be generated from the one or more initial graphs in the first graph data structure to produce a second graph data structure. The first and second graph can be used to train the model.

Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Abstract: Techniques and apparatus for data networking are described. In one example, a method includes receiving a first Layer-2 Remote Direct Memory Access (RDMA) packet which includes a virtual local area network (VLAN) tag and a quality-of-service (QoS) data field; converting the first Layer-2 RDMA packet to a first Layer-3 encapsulated packet; and forwarding the first Layer-3 encapsulated packet to a switch fabric. In this method, the converting includes adding at least one header to the first Layer-2 RDMA packet, where the at least one header includes: a virtual network identifier that is based on information from the VLAN tag, and a QoS value that is based on information from the QoS data field.

Abstract: Methods for generating and utilizing a multi-modal discourse tree (MMDT) are provided herein. An extended discourse tree (EDT) may be generated (e.g., from a discourse tree (DT) or a communicative DT (CDT)) from a corpus of text. Data records (e.g., records contained numerical data) may be linked to the extended discourse tree to generate a multi-modal discourse tree. The multi-modal discourse tree may link any suitable text/records from disparate sources. For example, entities identified from elementary discourse units of the EDT may be matched to an entity of a data record. Causal links may be identified between EDTs and/or data records. Rhetorical relationships can be identified for each entity/causal link match to incorporate the data records with the EDT to generate a MMDT. The MMDT may be used to classify subsequent input, to generate answers to subsequent questions, to navigate the corpus of text and/or data records, or the like.

Abstract: Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques for detecting and remediating anomalous intervals in time-series data of a monitored device are disclosed. A system trains a machine learning model on a combination of real data obtained from a monitoring device and false data generated by adding noise to the real data. The model predicts operating values for the device at individual intervals of a time-series data set. The system identifies anomalies in the time-series data based on differences between the predicted values and the real values. If the difference between a predicted value generate by the machine learning model and the real value exceeds a threshold, the system identifies a particular data point, such as a meter reading, as anomalous. The system ranks anomalies to perform remediation operations based on the ranking.

Abstract: A computer-implemented method to determine form factors of a tree includes building an input tree, wherein the input tree includes nodes. The method further includes implementing a first top-down pass to determine a universal number for each node in the input tree. The method further includes implementing a second top-down pass to determine form factors for each node of the input tree, wherein a form factor includes a depth and a width of the tree with a root in a corresponding node. The method further includes storing the form factors as part of node metadata or in a separate table.

Abstract: The present disclosure relates to systems and methods for automatic rule generation based on natural language input. Natural language input can be received. The natural language input can be tokenized. First tokens can be mapped to a first condition of a rule, and second tokens can be mapped to a second condition of the rule. A graph representation of the natural language input can be generated. A pre-generated, tenant-specific graph can be selected that corresponds to the graph representation of the natural language input. A rule can be generated based on the tenant-specific graph. The rule can be provided to facilitate implementation of the rule.

Abstract: Systems for repairing parse errors in query language statements are disclosed. In response to identifying one or more errors during parsing, a system can perform error recovery operations, including selecting a target sequence of parsable elements from candidate target sequences identified by a grammar. The operations can also include, determining a modification for the first sequence of elements including parsable elements corresponding to the target sequence. The operations can further include repairing first query by applying the modification to the first sequence. Additionally, the operations include generating error report, including information indicating the modifications that allowed parsing to succeed and suggested corrections.