Querying by Spatial Structure

A method and corresponding apparatus for monitoring high impedance failures in chip interconnects use monitoring circuitry on a chip to provide accurate and pro-active prediction of interconnect failures. The apparatus may include a resistance continuity monitoring circuit (RCMC), and a signal path connecting a representative set of pins to the RCMC. The RCMC measures the resistance of a connection of the representative set of pins with a circuit board during system operation and outputs a measured resistance data. The apparatus further includes additional analog-to-digital (A/D) hardware to perform an analog to digital conversion of the measured resistance data. The apparatus further includes a system interface for connecting the monitoring circuitry with other system management devices. The method then performs an algorithm on the measured resistance data, potentially warning of likely interconnect failures. The algorithm may include comparing the measured resistance data with a known threshold resistance value. Alternatively, the method displays and logs the measured resistance data for further study and analysis.

[1]  Max J. Egenhofer,et al.  Reasoning about Gradual Changes of Topological Relationships , 1992, Spatio-Temporal Reasoning.

[2]  Timos K. Sellis,et al.  Topological relations in the world of minimum bounding rectangles: a study with R-trees , 1995, SIGMOD '95.

[3]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[4]  Max J. Egenhofer,et al.  Similarity of Spatial Scenes , 1998 .

[5]  Shi-Kuo Chang,et al.  Iconic Indexing by 2-D Strings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Vijay V. Raghavan,et al.  Design and evaluation of algorithms for image retrieval by spatial similarity , 1995, TOIS.

[7]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[8]  Oliver Günther,et al.  Efficient computation of spatial joins , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[9]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[10]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.

[11]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

[12]  Rina Dechter,et al.  Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition , 1990, Artif. Intell..

[13]  John R. Smith,et al.  Searching for Images and Videos on the World-Wide Web , 1999 .

[14]  Hans-Peter Kriegel,et al.  Multi-step processing of spatial joins , 1994, SIGMOD '94.

[15]  Jack A. Orenstein.  Spatial query processing in an object-oriented database system , 1986, SIGMOD '86.

[16]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.

[17]  Fahiem Bacchus,et al.  On the Forward Checking Algorithm , 1995, CP.

[18]  Euripides G. M. Petrakis,et al.  Similarity Searching in Medical Image Databases , 1997, IEEE Trans. Knowl. Data Eng..

[19]  Anne H. H. Ngu,et al.  Picture Similarity Retrieval Using 2D Projection Interval Representation , 1996, IEEE Trans. Knowl. Data Eng..

[20]  Fahiem Bacchus,et al.  Dynamic Variable Ordering in CSPs , 1995, CP.

[21]  SUH-YIN LEE,et al.  Spatial reasoning and similarity retrieval of images using 2D C-string knowledge representation , 1992, Pattern Recognit..

[22]  James Allan,et al.  Automatic structuring and retrieval of large text files , 1994, CACM.

[23]  Doron Rotem.  Spatial join indices , 1991, [1991] Proceedings. Seventh International Conference on Data Engineering.

[24]  Daniel Hernández,et al.  Qualitative Representation of Spatial Knowledge , 1994, Lecture Notes in Computer Science.

[25]  Dimitris Papadias,et al.  Spatial Relations, Minimum Bounding Rectangles, and Spatial Data Structures , 1997, Int. J. Geogr. Inf. Sci..

[26]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[27]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[28]  Georgios Evangelidis,et al.  The hBP-tree: A Modified hB-tree Supporting Concurrency, Recovery and Node Consolidation , 1995, VLDB.

[29]  Rina Dechter,et al.  In Search of the Best Constraint Satisfaction Search , 1994, AAAI.

[30]  Hans-Peter Kriegel,et al.  Parallel processing of spatial joins using R-trees , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[31]  Christian Freksa,et al.  Temporal Reasoning Based on Semi-Intervals , 1992, Artif. Intell..

[32]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[33]  Elke A. Rundensteiner,et al.  Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations , 1997, VLDB.