Abstract: The systems and methods described here can reduce the storage space required (memory and/or disk) to store certain types of data, provide efficient (fast) creation, modification and retrieval of such data, and support such data within the framework of a multi-version database. In some embodiments, the systems and methods can store each field of a set of records as a vector of values, e.g., a data vector. A set of records can be represented using a vector hash vector, or “vhash” vector, wherein each element of the vhash vector contains a unique identifier of a data vector, based on a cryptographic hash of the data vector. A header table can store associations between labels and “vhash” vectors that pertain to those labels. Identical data vectors can be re-used between different record sets or vhash vectors needing that vector, thus saving space.

Abstract: The systems and methods described here can reduce the storage space required (memory and/or disk) to store certain types of data, provide efficient (fast) creation, modification and retrieval of such data, and support such data within the framework of a multi-version database. In some embodiments, the systems and methods can store each field of a set of records as a vector of values, e.g., a data vector. A set of records can be represented using a vector id vector, or “vid” vector, wherein each element of the vid vector contains a reference to the memory location of a data vector. A header table can store associations between labels and “vid” vectors that pertain to those labels. Identical data vectors can be re-used between different record sets or vid vectors needing that vector, thus saving space.

Abstract: A method to store a data value onto a cache of a storage hierarchy. A range of a collection of values that resides on a first tier of the hierarchy is initialized. The range is partitioned into disjointed range partitions; a first subset of which is designated as cached; a second subset is designated as uncached. The collection is partitioned into a subset of uncached data and cached data and placed into respective partitions. The range partition to which the data value belongs (i.e. the target range partition) is identified as being cached. If the cache is full, all of the disjointed range partitions are deleted. A first new cached partition range that contains the data value is created; it excludes at least one value that had been cached. The remaining values are placed in uncached range partitions; contents of the cache are updated to reflect the new range partition.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods that provide visualization of networks. Data is input into a table structure that represents any hierarchy of entities, relationships and their attributes. The content of the table is processed to extract the entities, relationships and their attributes. These are turned into nodes, edges and a visual representation of their attributes using color gradients, categorical colors, shapes, thickness, text labels, etc.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: A method to store a data value onto a cache of a storage hierarchy. A range of a collection of values that resides on a first tier of the hierarchy is initialized. The range is partitioned into disjointed range partitions; a first subset of which is designated as cached; a second subset is designated as uncached. The collection is partitioned into a subset of uncached data and cached data and placed into respective partions. The range partition to which the data value belongs (i.e. the target range partition) is identified as being cached. If the cache is full all cached range partitions that do not contain the data value are designated as uncached. All values that lie in the cached range partitions designated as uncached are evicted. The data value is then inserted into the target range partition, and copied to the first tier.

Abstract: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

Abstract: A method and system for a machine learning duster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

Abstract: A method to store a data value onto a cache of a storage hierarchy. A range of a collection of values that resides on a first tier of the hierarchy is initialized. The range is partitioned into disjointed range partitions; a first subset of which is designated as cached; a second subset is designated as uncached. The collection is partitioned into a subset of uncached data and cached data and placed into respective partitions. The range partition to which the data value belongs (i.e. the target range partition) is identified as being cached. If the cache is full, the range of the target range partition is reduced until either: the data value is excluded (if the data value is an end point of the partition range); or elements within the target range are evicted to make space for the data value.

Abstract: Systems and methods for obtaining product information via a conversational user interface. The communication channel receives communication from a user, the intent and entities of which are deduced by the NLP. These are communicated by the fulfillment API to the knowledge engine which retrieves information that fulfills the intent. The information is communicated to the fulfillment API, which converts the intent into a response, which in turn is forwarded by the NLP to the communication channel, and back to the user.

Abstract: A method to store a data value onto a cache of a storage hierarchy. A range of a collection of values that resides on a first tier of the hierarchy is initialized. The range is partitioned into disjointed range partitions; a first subset of which is designated as cached; a second subset is designated as uncached. The collection is partitioned into a subset of uncached data and cached data and placed into respective portions. The range partition to which the data value belongs (i.e. the target range partition) is identified as being cached. If the cache is full, the target range partition is divided into two partitions, the partition that excludes the data value is designated as uncached; the values therein are evicted. If the cache has space, the data value is copied onto the cache; otherwise the division/eviction are repeated until the cache has space.

Abstract: Systems and methods for computer memory management by a memory coordinator and a plurality of memory consumers. An urgency and memory quota of each memory consumer is initialized by the memory coordinator, which then adjusts the memory quota of each memory consumer such that the sum of the memory quota of each memory consumer does not exceed a finite amount of computer memory. Each memory consumer adjusts its memory usage in response to the quota input and urgency input from the memory coordinator.

Abstract: A method to store a data value onto a cache of a storage hierarchy. A range of a collection of values that resides on a first tier of the hierarchy is initialized. The range is partitioned into disjointed range partitions; a first subset of which is designated as cached; a second subset is designated as uncached. The collection is partitioned into a subset of uncached data and cached data and placed into respective partitions. The range partition to which the data value belongs (i.e. the target range partition) is identified as being cached. If the cache is full all cached range partitions that do not contain the data value are designated as uncached. All values that lie in the cached range partitions designated as uncached are evicted. The data value is then inserted into the target range partition, and copied to the first tier.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information.

Abstract: Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases.

Abstract: A query-based isolator comprising an observer, a collator and an extractor. The isolator reduces the database on which the query is run, to a new smaller, database on which the query can be performed, with the same result obtained on the smaller database, as that obtained on the original database.

Abstract: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

Abstract: A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.