International Business Machines Corporation

Abderrahim Labbi

Michail Vlachos

Peter Willem Jan Staar

Nikolaos Livathinos

Maksym Lysak

Viktor Kuropiatnyk

Cesar Berrospi Ramis

Anastasios Zouzias

Francesco Fusco

Mark Ian Lutwyche

Ulrich B Schimpel

Anton Zorin

Christos Boutsidis

Richard A Boedi

Walter Hehl

A computing system, program products and computer-implemented method of exploring data comprises accessing data structures and executing an interaction loop. Data structure captures connected graph nodes associated with attributes containing human-readable data. Interaction loop receives user-selected attributes associated with focal nodes and executes subroutines to determine layouts of an arborescence extending from focal nodes. The subroutines use starting nodes and pivot attributes as arguments. Identifying nodes of subroutines are connected to starting nodes by walking the graph and comparing pivot attributes with attributes associated with connected nodes, obtaining distance-dependent quantities. Subroutines compute layout data, including coordinates of the connected nodes. Coordinates are determined in accordance with the distance-dependent quantities. Interaction loop displays arborescence according to the layout data. Subroutines can be recursively executed, whereby, at each recursion level of execution, it is called with the selected attribute and each node of a selection of nodes, as a starting node.

Visually exploring implicit features of hierarchical graphs based on attributes of nodes of the graphs

A method for creating a database for a domain specific application includes providing a centralized data repository comprising data from different sources, identifying a set of data units of the repository that represent a specific domain, determining a pivotal entity type for an application, determining a mapping between different identifiers of the pivotal entity type, creating a reference set by selecting a first subset of the set of data units using the mapping, wherein the first subset represents the pivotal entity type, selecting, based at least in part on the mapping, a second subset of the set of data units, wherein the second subset represents non-pivotal entity types which are related to instances of the pivotal entity type in the reference set; and creating a database from data units and associated attributes selected from the reference set of data units and the second subset of data units.

Preparing a database for a domain specific application using a centralized data repository

A tool for managing text-item recognition systems such as NER (Named Entity Recognition) systems. The tool applies the system to a text corpus containing instances of text items, such as named entities, to be recognized by the system, and selecting from the text corpus a set of instances of text items which the system recognized. The tool tokenizes the text corpus such that each instance in the aforementioned set is encoded as a single token and processing the tokenized text via a word embedding scheme to generate a word embedding matrix. The tool, responsive to selecting a seed token corresponding to an instance in the aforementioned set, performs a nearest-neighbor search of the embedding space to identify a set of neighboring tokens for the seed token, and identifies the text corresponding to each neighboring token as a potential instance of a text item to be annotated.

Management of text-item recognition systems

The exemplary embodiments disclose a system and method, a computer program product, and a computer system. The exemplary embodiments may include receiving a data analysis request, using a knowledge graph for determining a source dataset based on the received data analysis request, wherein the knowledge graph represents an extract, transform and load (ETL) based ontology, wherein the knowledge graph comprises nodes representing entities and edges representing relationships between the entities, and wherein the entities are instances of concepts of the ETL based ontology, building an ETL workflow for processing the source dataset in accordance with a data lineage associated with the source dataset in the knowledge graph, and executing the ETL workflow.

Automated ETL workflow generation

Real-time rendering representations of objects in a view. Objects include clusters of nodes of a graph structure and links between said clusters. The graph structure comprises edges defined as pairs of the nodes. User inputs in respect to a current view and/or at least one of the objects is received. A data structure is updated, which associates multiple identifiers. Multiple identifiers may include cluster identifiers, node identifiers, and link identifiers, which respectively identify clusters of nodes, nodes of said graph, and links between pairs of clusters.

Easy clustering of graphs of nodes preserving node relationships

A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

Guided keyword-based exploration of data

A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, e, e) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K&#xd7;M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.

Identifying relationships between entities using two-dimensional arrays of scalar elements and a block matrix and displaying dense blocks

Identifying relationships between entities using two-dimensional array of scalar elements, and a block matrix

Exemplary embodiments include a method for re-ordering and visualizing a matrix in the presence of a data hierarchy stored on a computer system, the method including receiving a matrix, in the computer system, the matrix having rows and columns, reordering the matrix so that groups of related rows and columns are brought in adjacent matrix positions, wherein reordering the matrix A obeys constraints imposed by the data hierarchy on at least one of the rows and the columns and reordering the hierarchy on at least one of the rows and columns, wherein affinities in the data hierarchy are extracted by the reordering of the data hierarchy.

Matrix re-ordering and visualization in the presence of data hierarchies

A method, system and computer product for visualizing affinities between objects. The method includes the steps of: forming a representation of a minimum spanning tree where the minimum spanning tree connects the plurality of objects based on a pairwise distance between the plurality of objects; forming a hierarchical cluster of the plurality of objects where the hierarchical cluster includes a level; agglomerating the plurality of objects based on the pairwise distance; displaying a view of the representation of the minimum spanning tree in a graphical user interface; receiving a user selection of a parameter containing a hierarchical level; and identifying, in the view, a target cluster that corresponds to the hierarchical level; where at least one of the steps is carried out using a computer device so that affinities between the plurality of objects are visualized.

Computer-implemented visualization method

Method for dynamically allocating a cost to an e-mail that a first user of a transmission network such as the Internet network or an Intranet network who is a member of a group, wants to forward to a second user of the transmission network who is also a member of the group. The method comprising the steps of forwarding by the first user the e-mail to a market engine in charge of allocating a cost to the e-mail, determining by the market engine the cost of the e-mail based upon intrinsic and extrinsic criteria, submitting the cost of the e-mail to the first user for agreement, forwarding by the market engine the e-mail to the second user, and updating a credit account associated with the first user.

Cost-based method for dynamically pricing and prioritizing an e-mail

This invention provides techniques, systems and apparatus for assessing risk associated with a project or a portfolio of projects. An example embodiment having a processor and a memory, includes storing operational values which corresponds to a different project. Each operational value is representative of a project operational parameter. A risk vector is generated for each project based on the set of operational parameters. The risk vectors are partitioned into groups of similar risk vectors having a set of membership rules. Probabilities that a new project belongs to respective groups are generated. A risk value indicative of the risk associated with the new project is generated based on the probabilities generated. In some embodiments, the technique also assesses portfolio risk and utilizes an optimization algorithm to estimate acceptable or desirable margins to charge for each project in a portfolio of projects.

Project risk assessment

The invention is directed to a method for assigning to a set of products a set of corresponding continuous demand densities. In a conversion step for each product its demand time series is converted into a discrete demand density. In a normalization step the discrete demand densities are transformed into normalized discrete demand densities. In a clustering step each of the normalized discrete demand densities is assigned to a cluster and for each cluster a cluster-representative discrete density is determined. In a selection step for each cluster-representative discrete density out of a predetermined set of continuous model densities a cluster-representative continuous density is selected. In a parameter-determination step for each product for its cluster-representative continuous density product-individual density parameters are determined under use of which for each product a continuous density is determined. In an adjustment step for each product the continuous density is adjusted into the continuous demand density.

Growing community of inventors

Zurich, Switzerland

Abderrahim Labbi