Abstract: A method, system, and computer-readable code for a technique with which files encoded according to the Extensible Markup Language (XML) notation can be marked up to indicate that the content of the file (or some portion thereof) is dynamic in nature and is to be updated automatically to reflect changing information. The proposed technique provides a novel way to specify that a data repository should be accessed as the source of the updates. Techniques are defined for specifying that this data repository access occurs once, and for specifying that it occurs when a set of conditions are satisfied (which may include periodically repeating the data repository access and content update). In one aspect, the data repository is a database; in another aspect, the data repository is a file system. Preferably, the Lightweight Directory Access Protocol (LDAP) is used as an access method when the data repository being accessed is a database storing an LDAP directory.

Abstract: A method and system for dynamically searching databases in response to a query is provided by the present invention. More specifically, a system and method for dynamic data-mining and on-line communication of customized information. This method includes the steps of first creating a search-specific profile. This search-specific profile is then input into a data-mining search engine. The data-mining search engine will mine the search-specific profile to determine topic of interests. These topics of interest are output to at least one search tool. These search tools match the topics of interest to at least one destination data site wherein the destination data sites are evaluated to determine if relevant information is present in the destination data site. Relevant information is filtered and presented to the user making the inquiry.

Abstract: A system and method for merging static data and dynamic data downloaded to a browser from a network site of a network, preferably being a website on the Internet. The system includes at least one server in communication with the network, and the server hosts one or more network sites that include static data and dynamic data. The system further includes at least one user computer in communication with the network with a browser on the user computer that selectively interacts with network sites and selectively downloads the static data and dynamic data from the interacted network site. The user computer stores the static data and dynamic data in a local data store and recognizes the static data and dynamic data, preferably through marking with pointers, and upon refreshing of the downloaded data being requested, the user computer downloads dynamic data from the network site. The system provides a method including the steps of downloading static and dynamic data to the user computer from an interacted network site, recognizing the static data and dynamic data, storing the static data and dynamic data in a local data store of the user computer, requesting the refreshing of all downloaded data on the user computer from the network site, and downloading the dynamic data from the network site.

Abstract: A method for delivering and displaying dynamic data using information management software, wherein information is extracted from a dynamic datastream and stored in a server folder for user viewing with an information appliance. A dynamic view manager allows the user to create multi-framed views and save those views for future use.

Abstract: Various techniques have been developed to improve the performance of wireless information services. Techniques such as information broadcasting, caching of frequently accessed data, and point-to-point channels for pull-based data requests are often used to reduce data access time. To efficiently utilize information broadcast, indexing and scheduling schemes are employed for the organization of data broadcast. Most of the studies in the literature focused either on individual technique or a combination of them with some restrictive assumptions. There is no study considering these techniques working together in an integrated manner. In this paper, we propose a dynamic data delivery model for wireless communication environments. An important feature of our model is that data are disseminated through various storage mediums according to the dynamically collected data access patterns. Various results are presented in a set of simulation studies, which give some of the intuitions behind the design of a wireless data delivery system.

Abstract: A method operative at a server in response to a client browser request for generating a table having dynamic data The method begins by instantiating a table format object with dynamic data extracted from one of a set of data objects through a common interface The common interface enables page developers to author customized data getter objects that are used to access data in the set of data objects The dynamic data extracted from the given data object through the common interface is formatted into the table according to the table format object The resulting table with dynamic data is then served back to the requesting client browser

Abstract: The present invention proposes effective solutions for the design of Virtual Memory Management for applications with dynamic data types in an embedded (HW or SW) processor context. A structured search space for VMM mechanisms with orthogonal decision trees is presented. Based on said representation a systematic power exploration methodology is proposed that takes into account characteristics of the applications to prune the search space and guide the choices of a VMM for data dominated applications. A parameterizable model, called Flexible Pools, is proposed. This model limits the exploration of the Virtual Memory organization considerably without limiting the optimization possibilities.

Abstract: Information broadcasting, caching of frequently accessed data, and pull-based data delivery are commonly used techniques to reduce data access time of wireless information services. Most of the studies in the literature focused either on individual technique or a combination of them with some restrictive assumptions. In this paper, we propose a dynamic data delivery model where these three techniques work together in an integrated manner. A particular feature of our model is that data are disseminated through various storage mediums according to the dynamically collected data access patterns. Performance evaluation of the model is conducted by simulation studies.

Abstract: The e cient usage of workstations clusters depends rst of all on the distribution of the workload The following paper introduces a method to obtain e cient load balancing for data parallel applications through dynamic data assignment and a simple priority mechanism on a heterogeneous cluster of workstations assuming no prior knowledge about the workload This model improves the performance of load balancing methods in which one or more control processes remain idle for an extended period of time In order to investigate the performance of this method we take into consideration a problem of D image reconstruction that arises from events detected by a data acquisition system Studies of our load balancing model are performed under slight and heavy load condition Experimental results demonstrate that this model yields a substantial load balance even more if workstations are heavily loaded from exploiting the idle time of one control process In addition this strategy reduces the overhead due to communication so that it could be successfully employed in other dynamic balancing approaches

Abstract: Dynamic SPECT is a relatively new technique that may potentially benefit many imaging applications. Though similar to dynamic PET, the accuracy and precision of dynamic SPECT parameter estimates are degraded by factors that differ from those encountered in PET. In this work we formulate a methodology for analytically studying the propagation of errors from dynamic projection data to kinetic parameter estimates. This methodology is used to study the relationships between reconstruction estimators, image degrading factors, bias and statistical noise for the application of dynamic cardiac imaging with 99mTc-teboroxime. Dynamic data were simulated for a torso phantom, and the effects of attenuation, detector response and scatter were successively included to produce several data sets. The data were reconstructed to obtain both weighted and unweighted least squares solutions, and the kinetic rate parameters for a two-compartment model were estimated. The expected values and standard deviations describing the statistical distribution of parameters that would be estimated from noisy data were calculated analytically. The results of this analysis present several interesting implications for dynamic SPECT. Statistically weighted estimators performed only marginally better than unweighted ones, implying that more computationally efficient unweighted estimators may be appropriate. This also suggests that it may be beneficial to focus future research efforts upon regularization methods with beneficial bias-variance trade-offs. Other aspects of the study describe the fundamental limits of the bias variance trade-off regarding physical degrading factors and their compensation. The results characterize the effects of attenuation, detector response and scatter, and they are intended to guide future research into dynamic SPECT reconstruction and compensation methods.

Abstract: Systems and methods for controlling the dissemination of data from a repository based on the substitution of content contained within a generated response. The systems and methods are used by the repository to allow for the transfer of data that is not known or anticipated until the time of an access. For each access whose intent is to provide data to the repository or to retrieve variable data held by the repository, the repository uses the systems and methods to manage novel information structures whose purpose is to facilitate the substitution of the variable portions of content and to apply a privilege value to the portions that are variable. Using the concept of a “notational fragment”, the systems and methods are capable of assembling a variable result that has interaction with a data source. In addition, using a file structure known as a “Query Template Construct (“QTC”)” the systems and methods provide for the language syntax of a data source to have variable portions.

Abstract: A method to generate an efficient control law for a neural-network controller is presented to reduce the dynamic response of buildings exposed to earthquake-induced ground excitations. The proposed training scheme for the neural-network controller does not rely on the emulation of the structure to be controlled. The approach used for this work is based on a force-matching procedure, and it directly utilizes the dynamic data characterizing the structure response to generate an efficient training signal. The proposed controller has a feedback structure, utilizing a limited set of response quantities. A shear building actuated at its top by a tuned-mass damper is utilized to demonstrate the effectiveness of the controller. For training purposes, an ensemble of synthetically generated ground-motion time histories, with appropriate site spectrum characteristics, have been used. The performance of the trained controller is then evaluated for two different historic ground-acceleration records that do not belong to the training set of time histories. The numerical simulations show the control effectiveness of the proposed scheme with modest control requirements.

Abstract: This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS) A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS) In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes These data can be collected using a data acquisition device and stored in a dynamic database The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected The data are then structured into training data pairs to properly train an artificial neural network The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network The recommended control strategies will be transmitted to the MHS control for real-time execution If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates

Abstract: A great challenge for data mining techniques is the huge space of potential rules which can be generated If there are tens of thousands of items, then potential rules involving three items number in the trillions Traditional data mining techniques rely on downward-closed measures such as support to prune the space of rules However, in many applications, such pruning techniques either do not sufficiently reduce the space of rules, or they are overly restrictive We propose a new solution to this problem, called Dynamic Data Mining (DDM) DDM foregoes the completeness offered by traditional techniques based on downward-closed measures in favor of the ability to drill deep into the space of rules and provide the user with a better view of the structure present in a data set Instead of a single deterministic run, DDM runs continuously, exploring more and more of the rule space Instead of using a downward-closed measure such as support to guide its exploration, DDM uses a user-defined measure called weight, which is not restricted to be downward closed The exploration is guided by a heuristic called the Heavy Edge Property The system incorporates user feedback by allowing weight to be redefined dynamically We test the system on a particularlly difficult data set - the word usage in a large subset of the World Wide Web We find that Dynamic Data Mining is an effective tool for mining such difficult data sets

Abstract: Building reservoir geological models that are consistent with all available information is necessary to reduce uncertainties in production forecasts. The stochastic/geostatistical approach is the only feasible way of integrating all kinds of data ranging from the geological knowledge to the production history. A stochastic model is chosen that accounts for geological knowledge, and geostatistical simulation provides realizations of the stochastic model. These realizations can be conditioned to static data at well locations to reduce the model uncertainty in the area where data are available. All along the different stages of a field life more and more dynamic data are available. This paper addresses the issue of generating model realizations that account for available dynamic data to further reduce uncertainty in the underlying geological model. A general tool has been developed to build geostatistical realizations that match both the available geological and dynamic data. The core of this tool is the Gradual Deformation Method that allows consistent modification of a realization in a continuous way. This method can be combined with a nonlinear optimization method for constraining realizations of a stochastic model to dynamic data. This approach is operational for Gaussian or nonGaussian pixel based models. Model realizations can be deformed globally or locally while preserving the geostatistical characteristics. Several examples are provided to illustrate different features of the gradual deformation approach. Introduction Geostatistics, originally developed for mining industry, has been increasingly gaining success in the petroleum industry for the last twenty years. This is mainly because geostatistics provides the potentiality of consistently integrating various sources of information at different scales and also the possibility of efficiently generating multiple realizations for uncertainty evaluation. Several methods and algorithms have been developed for building reservoir models in a wide range of geological environments. Although geostatistical models help geologists doing better 3D interpretation of geological phenomena, reservoir engineers still not commonly use them for performing history matching and production forecasting. The main obstacles for the use of geostatistics by reservoir engineers are the huge number of parameters in a geostatistical model and the lack of an efficient tool for consistent modification of geostatistical realizations. This led us to develop the gradual deformation method. In this paper, we first present the principle of the gradual deformation of geostatistical models. Then, we review the most widely used geostatistical algorithms for generating reservoir geological models. We focus on the applicability of gradual deformation method to these simulation algorithms. Finally, we describe the inverse approach that combines an optimization method and the gradual deformation method for constraining or updating geostatistical reservoir models. Gradual deformation methods Geostatistical simulation can provide independent realizations of a stochastic reservoir model, which are consistent with geological and geophysical information. However, in most cases, these realizations do not reflect the dynamic behavior observed on the real field. Reservoir engineers need to modify these realizations for matching well-test data and production history. Traditional practice of history matching, by zonation, multiplication, adding permeability barriers etc., destroys the consistency and the spatial continuity of the initial stochastic model. This often leads to reservoir models with low predictability. Consequently, this prevents geostatistics from SPE 56703 Reducing Uncertainties in Production Forecasts by Constraining Geological Modeling to Dynamic Data L.Y. Hu, M. Le Ravalec, G. Blanc, F. Roggero, B. Noetinger, Institut Français du Pétrole, A. Haas, B. Corre, Elf Exploration & Production 2 L.Y. HU ET AL. SPE 56703 being routinely used by reservoir engineers. It has been urgent to build an efficient tool for modifying a realization while keeping it consistent with the geological information. The gradual deformation method was born and developed in such a background. Gradual global deformation. The idea for building deformation tool is to consider the stochastic reservoir model as a spatio-temporal process. At any time, the state of this process corresponds to a realization of the reservoir model. In the Gaussian framework, a convenient way of building a spatio-temporal Gaussian random function consists in combining two independent standard Gaussian random functions Y1 and Y2 with identical covariance: Y t Y t Y t ( ) cos sin = + 1 2 It can be shown that, for any t , Y t ( ) is a standard Gaussian random function which shares the covariance of Y1 and Y2 . Given two independent realizations of Y1 and Y2 , we get a continuous chain of realizations of Y t ( ) . The basic idea of the gradual deformation method is to modify realizations by changing parameter t . Figure 1 shows an example of a continuous train of realizations. By gradually deforming a Gaussian realization, we gradually deform all Gaussian-related models (e.g. lognormal model, truncated Gaussian model etc.). Moreover, instead of using two independent realizations, it is possible to create a multidimensional process of realizations by using several independent realizations. This provides more flexibility for deforming realizations 14. Gradual structural deformation. In many cases, there is not enough data to infer accurately the structural parameters of a stochastic model (e.g. its mean, variance, covariance function etc.). These structural parameters are often given in terms of intervals or a priori distributions. Therefore they must also be considered as fitting parameters. This involves the deformation of a stochastic model with respect to its structural parameters. Most of the stochastic simulation algorithms require at least the specification of the covariance before generating realizations. In order to deform a realization while modifying simultaneously its structural parameters, it is necessary to use a stochastic simulation algorithm that separates the generation of random numbers from the imposition of a structure 15. Denote by X a standard Gaussian white noise and L the covariance operator, then

Abstract: We present a new exploration and optimization method to select customized implementations for dynamic data sets, as encountered in telecom network, database and multimedia applications. Our method fits in the context of embedded system synthesis for such applications, and enables to further raise the abstraction level of the initial specification, where dynamic data sets can be specified without low-level details. Our method is suited for hardware and software implementations. In this paper, it mainly aims at minimizing the memory power consumption, although it can also be driven by other cost functions such as area or performance. Compared with existing methods, it can save up to 2/3 of the memory power consumption and 3/4 of the memory area.

Abstract: In this paper, we propose four slicing methods using both static and dynamic analysis information. (1) Statement-mark slice removes the unnecessary statements using an execution history of the statements. (2) Partial program analysis reduces the static analysis cost using invocation history of procedures. (3) Dynamic data dependence analysis extracts precise data dependence relations using dynamic data dependence analysis. (4) Array and pointer analysis improves the efficiency of (3) by dynamically analyzing pointer and array variables only. Using both dynamic and static information, we show that the precision of the slicing is improved with smaller run-time overhead.

Abstract: Slow rotation acquisition of dynamic data has several advantages over fast rotation acquisition which is currently the method of choice used for the acquisition of dynamic data in SPECT. Slow rotation is currently not used because of error from inconsistent data. In this work, we develop a method of reconstructing from projections that are inconsistent in time due to being acquired during a slow acquisition. Our method is based on a factor model of physiological data. A series of dynamic images are reconstructed, where each reconstructed image corresponds in time to only one projection. Such an under-determined reconstruction is shown to be possible through utilization of a factor model. Computer simulations are performed using simple phantoms. We found that we are able to accurately reconstruct the dynamic sequence for simple phantoms with temporal behavior corresponding to teboroxime-Tc-99m heart imaging.

Abstract: This thesis is concerned with the application of model updating techniques to structures whose dynamic performance is affected by static loading. The influence of structural loading is identified as important in dynamical terms. As a result, experimentally determined dynamic data should not necessarily be regarded as being uniquely representative of a structure. This observation has particular significance to the likelihood of success of model updating. Indeed the major source of error in dynamic finite element modelling may well be the omission of static loads. There are strong repercussions for schemes which use dynamic data for damage detection. The methods for including load-effects in dynamic finite element models are outlined. A “static updating” technique which takes into account nonlinear geometry effects is demonstrated by means of experimental case studies. This technique is shown to produce finite element models of structures which match measured dynamic data. Two new types of updating parameters are introduced in this thesis which allow the effects of loading to be accounted for in a finite element model. The parameters are shown to be suitable for implementation in the popular sensitivity based approach to updating. The first parameter accounts for the effects of stress stiffening in struts or beams and the second allows rigid body rotation of these types of elements. The changes made to beam type elements, while physically realistic, are shown not to be members of the generic beam element family. The stress stiffening parameter is shown to allow loading in structural frames to be identified from dynamic data even with no a priori knowledge of load state. The rigid body rotation term is shown in the case of a 2D beam to allow identification of deformation given limited initial knowledge of the deflected shape. An extension of the latter technique is developed which allows the magnitude of pre-defined deflection shapes or “profiles” to be updated. An experimental case study is presented in which success is achieved in updating a finite element model of the structure to account for load effects. Axial loads identified in a frame are shown to compare satisfyingly with static measurements of loading. The convergence is found to be reliable and robust, even when including other updating parameters. A strategy for model updating is proposed which includes the effects of static loading. The approach should be adopted whenever static loading is suspected of influencing dynamic behaviour.

Abstract: We propose an information push model information dissemination to mobile users, called dynamic channel model. It is a set of extensions to the conceptual models developed for information channels, capturing mobility-specific requirements. These are used for filtering and setting update order priorities for the information that users intend to monitor, which depends on their location and changes as they roam. We also present the architecture of Ubidata, an adaptable framework for information dissemination to mobile systems. We introduce our development process for a class of mobile computing applications operating under an information dissemination metaphor.

Abstract: Data warehouses and on-line analytical processing (OLAP) tools have become essential elements of decision support systems. Traditionally, data warehouses are refreshed periodically (for example, nightly) by extracting, transforming, cleaning and consolidating data from several operational data sources. The data in the warehouse is then used to periodically generate reports, or to rebuild multidimensional (data cube) views of the data for on-line querying and analysis. Increasingly, however, we are seeing business intelligence applications in telecommunications, electronic commerce, and other industries, that are characterized by very high data volumes and data flow rates, and that require continuous analysis and mining of the data. For such applications, rather different data warehousing and on-line analysis architectures are required. In this paper, we first motivate the need for a new architecture by summarizing the requirements of these applications. Then, we describe a few approaches that are being developed, including virtual data warehouses or enterprise portals that support access through views or links directly to the operational data sources. We discuss the relative merits of these approaches. We then focus on a dynamic data warehousing and OLAP architecture that we have developed and prototyped at HP Labs. In this architecture, data flows continuously into a data warehouse, and is staged into one or more OLAP tools that are used as computation engines to continuously and incrementally build summary data cubes, which might then be stored back in the data warehouse. Analysis and data mining functions are performed continuously and incrementally over these summary cubes. Retirement policies define when to discard data from the warehouse (i.e., move data from the warehouse into off-line archival storage). Data at different levels of aggregation may have different life spans depending on how they are to be used for downstream analysis and data mining. The key features of the architecture are the following: incremental data reduction using OLAP engines to generate summaries and enable data mining; staging large volumes and flow rates of data with different life spans at different levels of aggregation; and scheduling operations on data depending on the type of processing to be performed and the age of the data.

Abstract: PROBLEM TO BE SOLVED: To facilitate a proper action for the management of a plant by using the information collected from the plant in an on-line method as the dynamic data and preparing a dynamic data base consisting of the dynamic data and a static data base consisting of the static data excluding the dynamic data. SOLUTION: The process information 4 such as the information collected from a plant in an on-line method and the alarm information 5 are used as the dynamic data and stored in a dynamic data base 1. Meanwhile, the information acquired by a reader 7 consisting of a document 7a, the image information 7b and a scanner 7c are inputted to a static data base 2 as the static data excluding the dynamic data. Then the device information 6 is retrieved from the information 5 by a retrieving device 8 via both data bases 1 and 2 with key operations as the plant support information. Thus, it is possible to facilitate a proper action for the management of the plant regardless of the knowledge level of an operator, etc.

Abstract: PROBLEM TO BE SOLVED: To evaluate an object company at proper point of time for evaluation by performing the company evaluation on the basis of the inputs of an output evaluation calculated by using a static model and dynamic data. SOLUTION: A calculation part 230 for evaluation data by the static model calculates the grading numeral of the object company to be evaluated by applying data of the object company from an input part 240 to the static model and outputs the result 234. For the subsequence generation of a dynamic model, data of respective sample companies are also applied to the static model to calculate and output respective grating numerals 232. A dynamic model generation part 250 structures evaluation models as to the sample companies on the basis of the evaluation output 232 by the static model and the input 210 of the dynamic data. A calculation part 260 for evaluation data by the dynamic model applies the dynamic data of the object company to be evaluated from the input part 240 to the dynamic model together with the output 234 from the static model to calculate a rating variation value of the object company and outputs its evaluation data 270. COPYRIGHT: (C)2000,JPO

Abstract: PROBLEM TO BE SOLVED: To provide a data backup system for CNC device with which the dynamic data of high frequency can be backed up without using any external storage medium and auxiliary power source and data can be restored without erasing them because of an operation error. SOLUTION: When ordinarily operating a CNC device 1 in the data management of the CNC device 1, the dynamic data of the high reloading frequency stored in a volatile memory (SRAM 3) are backed up. In this case, when a voltage is lowered, the stored contents in the volatile memory (SRAM 3) are automatically written in a writable non-volatile memory (FROM 7) so that the data can be backed up.

Abstract: A method and apparatus in a data processing system for displaying data. A stream of data is received. The stream of data is displayed in a scrolling display field in which data is scrolling. Responsive to a user input selecting a portion of the data in the stream of data being displayed, scrolling of the data is halted in the scrolling display field. A stationary display field is created. Scrolling of the data on the scrolling display field is restored. Responsive to changes in the selected data in the stream of data being received, the scrolling display field and the stationary display field are updated.

Abstract: Data warehouses and on-line analytical processing (OLAP) tools have become essential elements of decision support systems. Traditionally, data warehouses are refreshed periodically (for example, nightly) by extracting, transforming, cleaning and consolidating data from several operational data sources. The data in the warehouse is then used to periodically generate reports, or to rebuild multidimensional (data cube) views of the data for on-line querying and analysis. Increasingly, however, we are seeing business intelligence applications in telecommunications, electronic commerce, and other industries, that are characterized by very high data volumes and data flow rates, and that require continuous analysis and mining of the data. For such applications, rather different data warehousing and on-line analysis architectures are required. In this paper, we first motivate the need for a new architecture by summarizing the requirements of these applications. Then, we describe a few approaches that are being developed, including virtual data warehouses or enterprise portals that support access through views or links directly to the operational data sources. We discuss the relative merits of these approaches. We then focus on a dynamic data warehousing and OLAP architecture that we have developed and prototyped at HP Labs. In this architecture, data flows continuously into a data warehouse, and is staged into one or more OLAP tools that are used as computation engines to continuously and incrementally build summary data cubes, which might then be stored back in the data warehouse. Analysis and data mining functions are performed continuously and incrementally over these summary cubes. Retirement policies define when to discard data from the warehouse (i.e., move data from the warehouse into off-line archival storage). Data at different levels of aggregation may have different life spans depending on how they are to be used for downstream analysis and data mining. The key features of the architecture are the following: incremental data reduction using OLAP engines to generate summaries and enable data mining; staging large volumes and flow rates of data with different life spans at different levels of aggregation; and scheduling operations on data depending on the type of processing to be performed and the age of the data.