International Business Machines Corporation

Raymond Lloyd

Olivier Verscheure

Joern Ploennigs

Anika Schumann

Jeremiah Patrick Hayes

Niall Brady

William Karol Lynch

Bernard Gorman

Hendrik F Hamann

Elizabeth Daly

Adi Ionel Botea

Wanli Min

Alice J Chang

Xiaoxi Tian

Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.

Management of building energy systems through quantification of reliability

Embodiments for identifying and evaluating white space target entities by a processor. Data may be collected from a knowledge domain relating to a plurality of entities and text data extracted from one or more data sources. An enterprise social network graph may be created having one or more nodes relating to the collected data. The one or more nodes in the enterprise social network graph may be ranked. One or more potential target nodes may be recommended to a selected node as a candidate for establishing a potential relationship according to the ranking.

Identification and evaluation white space target entity for transaction operations

A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.

Generating a ranked list of best fitting place names

Techniques for data center analysis are provided. In one aspect, a method for modeling thermal distributions in a data center is provided. The method includes the following steps. Vertical temperature distribution data is obtained for a plurality of locations throughout the data center. The vertical temperature distribution data for each of the locations is plotted as an s-curve, wherein the vertical temperature distribution data reflects physical conditions at each of the locations which is reflected in a shape of the s-curve. Each of the s-curves is represented with a set of parameters that characterize the shape of the s-curve, wherein the s-curve representations make up a knowledge base model of predefined s-curve types from which thermal distributions and associated physical conditions at the plurality of locations throughout the data center can be analyzed.

Knowledge-based models for data centers

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Navan, Ireland

Raymond Lloyd