Continuous media described by time-dependent data

Time-dependent data, defined by the attributesstart time andduration can describe continuous media. A short theoretical analysis of sets of time-dependent data forms the first part of this paper. The results are three normal forms of continuous media data. In the second part, a data model for continuous media calledSequence is defined according to the theory presented. Not only does this model implement the data structure, it also inculdes operations that apply generally to continuous media data. The third part of the paper is a short overview showing how to integrate time-dependent data asSequences into database systems.     

Synthesis of statistical knowledge from time-dependent data

A general approach to analyzing multivariate time-dependent system processes with discrete-valued (both nominal and ordinal) and/or continuous-valued outcomes is presented. The approach is based on an event-covering method which selects (or covers) a subspace from the outcome space of an n-tuple of variables for estimation purposes. From the covered subspace, statistically interdependent events are selected as statistical knowledge for forecasting unknown events. The event-covering method presented is based on the use of restricted variables with only a subset of the outcomes considered. An extension to the event-covering method based on the selection of joint outcomes is discussed. The testing of this method using climatic data and simulated data which model situations in real life is described. The experiments show that the method is able to detect statistically relevant information, describe it in a meaningful and comprehensible way, and use this information for a reliable estimation (or forecast) of the missing values that will occur at some future time     

Interval-based conceptual models for time-dependent multimedia data

Multimedia data often have time dependencies that must be satisfied at presentation time. To support a general-purpose multimedia information system, these timing relationships must be managed to provide utility to both the data presentation system and the multimedia author. New conceptual models for capturing these timing relationships, and managing them as part of a database are proposed. Specifically, n-ary and reverse temporal relations are introduced and defined along with their temporal constraints. These new relations are a generalization of earlier temporal models and establish the basis for conceptual database structures and temporal access control algorithms to facilitate forward, reverse, and partial-interval evaluation during multimedia object playout. The proposed relations are defined to ensure a property of monotonically increasing playout deadlines to facilitate both real-time deadline-driven playout scheduling or optimistic interval-based process playout. A translation of the conceptual models to a structure suitable for a relational database is presented     

Storage allocation policies for time-dependent multimedia data

Multimedia computing requires support for heterogeneous data types with differing storage, communication and delivery requirements. Continuous media data types, such as audio and video, impose delivery requirements that are not satisfied by conventional physical storage organizations. In this paper, we describe a physical organization for multimedia data based on the need to support the delivery of multiple playout sessions from a single rotating-disk storage device. Our model relates disk characteristics to the different media recording and playback rates and derives their storage pattern. This storage organization guarantees that as long as a multimedia delivery process is running, starvation will never occur. Furthermore, we derive bandwidth and buffer constraints for disk access and present an approach to minimize latencies for non-continuous media stored on the same device. The analysis and numerical results indicate the feasibility of using conventional rotating magnetic disk storage devices to support multiple sessions for video-on-demand applications     

A framework for synchronous delivery of time-dependent multimedia data

Multimedia data often have time dependencies that must be satisfied at presentation time. To support a general purpose multimedia information system, these timing relationships must be managed to provide utility to both the data presentation system and the multimedia author. Timing management encompasses specification, data representation, temporal access control, playout scheduling, and run-time intermedia synchronization. In this paper we describe the components of our framework for supporting time-dependent multimedia data encompassing these areas and how they are assembled into a unified system.     

Multidimensional data analysis and visualization for time-dependent CFD problems

Programming and Computer Software - This paper is devoted to analysis and visualization of multidimensional data in problems of computational fluid dynamics (CFD). Multidimensional data are...     

Visualization of time-dependent data with feature tracking and event detection

This paper presents an innovative method to analyze and visualize time-dependent evolution of features. The analysis and visualization of time-dependent data are complicated because of the immense number of data involved. However, if the scientist's main interest is the evolution of certain features, it suffices to show the evolution of these features. The task of the visualization method is to extract the features from all frames, to determine the correspondences between features in successive frames, to detect significant events or stages in the evolution of the features, and, finally, to visualize the results. The method described here performs all these steps, and it is applied to a number of applications.     

Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data

In the paper, an approach is described which permits the numerical, model-free prediction of uncertain time-dependent structural responses. Uncertain time-dependent structural actions and responses are modelled by means of fuzzy processes. The prediction approach is based on recurrent neural networks for fuzzy data trained by time-dependent results of measurements or numerical analyses. An efficient solution for network training and prediction is developed utilizing α-cuts and fuzzy arithmetic. The approach is verified using a fractional rheological model. The capability of the approach is demonstrated by predicting the long-term structural behaviour of reinforced concrete plates strengthened by textile reinforced concrete layers.     

Applying a kernel function on time-dependent data to provide supervised-learning guarantees

The Statistical Learning Theory (SLT) defines five assumptions to ensure learning for supervised algorithms. Data independency is one of those assumptions, once the SLT relies on the Law of Large Numbers to ensure learning bounds. As a consequence, this assumption imposes a strong limitation to guarantee learning on time-dependent scenarios. In order to tackle this issue, some researchers relax this assumption with the detriment of invalidating all theoretical results provided by the SLT. In this paper we apply a kernel function, more precisely the Takens’ immersion theorem, to reconstruct time-dependent open-ended sequences of observations, also referred to as data streams in the context of Machine Learning, into multidimensional spaces (a.k.a. phase spaces) in attempt to hold the data independency assumption. At first, we study the best immersion parameterization for our kernel function using the Distance-Weighted Nearest Neighbors (DWNN). Next, we use this best immersion to recursively forecast next observations based on the prediction horizon, estimated using the Lyapunov exponent. Afterwards, predicted observations are compared against the expected ones using the Mean Distance from the Diagonal Line (MDDL). Theoretical and experimental results based on a cross-validation strategy provide stronger evidences of generalization, what allows us to conclude that one can learn from time-dependent data after using our approach. This opens up a very important possibility for ensuring supervised learning when it comes to time-dependent data, being useful to tackle applications such as in the climate, animal tracking, biology and other domains.     

Parallel two-grid finite element method for the time-dependent natural convection problem with non-smooth initial data

In this paper, we consider the parallel two-grid finite element method for the transient natural convection problem with non-smooth initial data. Our numerical scheme involves solving a nonlinear natural convection problem on the coarse grid and solving a linear natural convection problem on the fine grid. The linear natural convection problem can be split into two subproblems which can be solved in parallel: a linearized Navier–Stokes problem and a linear parabolic problem. We firstly provide the stability and convergence of standard Galerkin finite element method with non-smooth initial data. Secondly, we develop optimal error estimates of two-grid finite element method for velocity and temperature in $H^1$-norm and for pressure in $L^2$-norm. In order to overcome the difficulty posed by the loss of regularity, some suitable weight functions are introduced in our stability and convergence analysis for the natural convection equations. Finally, some numerical results are presented to verify the established theoretical results.     

基于数据仓库的QAR数据分析

针对现有各航空公司分析QAR数据的平台不统一、QAR数据量大、分析和处理数据能力不足、导致海量的数据变成了信息垃圾,在对QAR数据和数据仓库技术研究基础上,结合航空公司关心的问题,设计了QAR数据仓库,给出了数据仓库模型的设计,详细阐述了QAR数据分析主题的提取方法,提出QAR数据仓库的星型模型.通过ETL设计了多维数据集,以超限事件为例展现了对QAR数据的安全分析,为航空公司改进飞行品质、提高安全系数提供依据.     

Analysis of architecture evaluation data

The output of 18 software architecture evaluations is analyzed. The goal of the analysis is to find patterns in the important quality attributes and risk themes identified in the evaluations. The major results are

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A categorization of risk themes.

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The observation that twice as many risk themes are risks of “omission” as are risks of “commission”.

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A failure to find a relationship between the business and mission goals of a system and the risk themes from an evaluation of that system.

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A failure to find a correlation between the domain of a system being evaluated and the important quality attributes for that system.

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A wide diversity of names used for various quality attributes.

The results of this investigation have application to practitioners by suggesting activities on which developers should put greater focus. They also have application to researchers by suggesting further areas of investigation.     

Conjugate problems in time-dependent scheduling

In the paper, we consider conjugate problems which constitute a new class of mutually related time-dependent scheduling problems. Any element from this class is a composite problem, being a pair of two time-dependent scheduling problems connected by a conjugacy formula. We prove basic properties of conjugate problems and show the relations that hold between such problems. We also propose an approach to the construction of greedy heuristics for the conjugate problems. We illustrate applications of the results by examples.     

Scheduling with time-dependent discrepancy times

In time-dependent scheduling, various processing time functions are studied, yet absolute value functions have surprisingly been omitted from the discussion. Such a processing time function increases linearly with a job’s discrepancy from its ideal midtime. The objective is to find a schedule that minimizes the makespan, introducing the discrepancy time minimization problem. This single-machine scheduling problem with time-dependent processing times is motivated by optimization of walking times on a car assembly line. Its decision version is NP hard, as we show by reduction of the even–odd partition problem. For the variant with known start time, we develop several heuristics. Further insights form lower bounds and dominance rules for a branch-and-bound search. Numerical experiments show the performance of our algorithms on problem instances of up to 60 jobs. For the variant with common ideal midtime and flexible start time, we present a polynomial-time algorithm.     

Supply disruptions with time-dependent parameters

We consider a firm that faces random demand and receives shipments from a single supplier who faces random supply. The supplier's availability may be affected by events such as storms, strikes, machine breakdowns, and congestion due to orders from its other customers. In our model, we consider a dynamic environment: the probability of disruption, as well as the demand intensity, can be time dependent. We model this problem as a two-dimensional non-homogeneous continuous-time Markov chain (CTMC), which we solve numerically to obtain the total cost under various ordering policies. We propose several such policies, some of which are time dependent while others are not. The key question we address is: How much improvement in cost is gained by using time-varying ordering policies rather than stationary ones?     

Approximation algorithms for time-dependent orienteering

The time-dependent orienteering problem is dual to the time-dependent traveling salesman problem. It consists of visiting a maximum number of sites within a given deadline. The traveling time between two sites is in general dependent on the starting time.For any ε>0, we provide a (2+ε)-approximation algorithm for the time-dependent orienteering problem which runs in polynomial time if the ratio between the maximum and minimum traveling time between any two sites is constant. No prior upper approximation bounds were known for this time-dependent problem.     

Analysis of a replicated data base

In this paper we study the performance characteristics of a replicated data base under two different updating policies. In the synchronous case requests for any replications of the data base can be processed only if no copies of data base are being updated due to a previous write request, whereas in the non-synchronous case read requests are allowed to be processed at any time if there is a free data base copy. We formulate a queueing theoretic model of the system assuming a Poisson arrival process for both read and write requests. This model is then solved using the matrix geometric solution method and the relevant performance metrics are derived and analyzed.     

Parallel implementation of time-dependent density functional theory

We present a massively parallel implementation of time-dependent density functional theory in real space, aimed at computing optical absorption spectra of realistic systems with hundreds of atoms from first principles. We provide details of the formalism and discuss its implementation, optimization, and efficient parallelization, as well as remaining limitations, in detail. The capabilities of the code are illustrated by calculations of optical properties of hydrogenated silicon quantum dots.