2448001
The Instance Store: DL Reasoning with Large Numbers of Individuals
We present an application – the Instance Store – aimed at solving some of the scalability problems that arise when reasoning with the large numbers of individuals envisaged in the semantic web. The approach uses well-known techniques for reducing description logic reasoning with individuals to reasoning with concepts. Crucial to the implementation is the combination of a description logic terminological reasoner with a traditional relational database. The resulting form of inference, although specialised, is sound and complete and sufficient for several interesting applications. Most importantly, the application scales to sizes (over 100,000s individuals) where all other existing applications fail. This claim is substantiated by a detailed empirical evaluation of the Instance Store in contrast with existing alternative approaches.
83373
Reasoning with large numbers of individuals moves on: extending the instance store
The Semantic Web [2] aims at enabling Web resources to be accessible to automated processes by adding “semantic annotations”—metadata (data about data) that describes their content. It is envisaged that the semantics in semantic annotations will be given by ontologies [8], which will provide a source of precisely defined terms (vocabularies) that are amenable to automated reasoning. A standard for expressing ontologies in the Semantic Web has already emerged: the ontology language OWL [4], which recently became a W3C proposed recommendation. One of the main features of OWL is that there is a direct correspondence between (two of the three “species” of) OWL and Description Logics (DLs) [12]. This means that reasoning procedures from DLs can be used in order to infer useful facts from ontology based annotations in OWL. In particular, since annotations are usually defined as individual instances of ontology classes, it is reasonable then to use ABox reasoning (the DL term for reasoning about individuals) to reason about annotations [3]. However, developing a practical ABox reasoner for such annotations is difficult. This difficulty arises not only from the computational complexity of ABox reasoning, but also from the fact that the number of annotations might be extremely large. On the one hand, the task of reasoning with large amounts of individuals is difficult even for the state-of-art optimised ABox reasoner RACER [9]. On the other hand, efficient management of very large volumes of data is well known to be a stronghold of relational databases. The approach of combining relational databases with Description Logics to deal with large amounts of data was, therefore, proposed in [11] which we call an Instance Store. The Instance Store provides an infrastructure for reasoning with large numbers of individuals. It combines a DL reasoner with a database in order to provide large scale storage of instance data, and sound and complete answers to instance retrieval queries for arbitrary query concepts. However, in order to simplify the integrated architecture, and to provide acceptable query answering performance, the current Instance Store only supports ABox axioms asserting that a given individual is an instance of a given (possibly complex) concept (e.g., John is an instance of the class of persons having at least one female child); but it forbids axiom assertions involving role relationships between pairs of individuals (e.g., an assertion that Mary is a child of John). Certainly, the idea of the Instance Store is not new, but, to the best of our knowledge, it is the first implementation of a general purposed role-free1 DL reasoner over individuals that is sound and complete, has reasonable response times. This claim is supported by presenting a variety of empirical test results [11] contrasting the Instance Store with the other existing techniques such as RACER.
27925816
A fuzzy approach to the multiobjective transportation problem
The aim of this paper is to present a fuzzy compromise programming approach to multiobjective transportation problems. A characteristic feature of the approach proposed is that various objectives are synthetically considered with the marginal evaluation for individual objectives and the global evaluation for all objectives. The decision-maker’s preference is taken into account by his/her assigning the weights of objectives. With the global evaluation for all objectives, a compromise programming model is formulated. This model covers a wide spectrum of methods with Zimmermann’s fuzzy programming approach essentially equivalent to one of its special cases. Using ordinary optimization technique, we solve the fuzzy compromise programming model to obtain a non-dominated compromise solution at which the synthetic membership degree of the global evaluation for all objectives is maximum. A numerical example is given to demonstrate the efficiency of the proposed fuzzy compromise programming approach. 
 
 
Scope and purpose 
 
In many real-world situations for transportation problems, decisions are often made in the presence of multiple, conflicting, incommensurate objectives. Intensive investigations on multiobjective linear transportation problem have been made. Among them Zimmermann’s fuzzy programming appears to be an ideal approach for obtaining the optimal compromise solution to a multiobjective transportation problem. However, due to the ease of computation, the aggregate operator used in Zimmermann’s fuzzy programming is the “min” operator, which does not guarantee a non-dominated solution. In this paper, we propose a new fuzzy compromise programming approach to multiobjective transportation problems. It is shown that the approach proposed can give a compromise solution that is not only non-dominated but also optimal in a certain sense. The proposed approach is robust enough to cover a wide spectrum of methods. Zimmermann’s fuzzy programming approach is essentially equivalent to one of the proposed method’s special cases.
20793791
Longitudinal data model selection
In longitudinal data with correlated errors, we apply the likelihood and residual likelihood approaches to obtain the corrected Akaike information criterion (AICc) and the residual information criterion (RIC), respectively. Simulation studies show that AICc outperforms the Akaike information criterion (AIC) when the numbers of subjects and repeated observations are small, and RIC is superior to the Bayesian information criterion (BIC) when the signal-to-noise ratio is moderate to large. We illustrate the practical use of these selection criteria with an empirical example for modeling the serum cholesterol measured at six time occasions.
14092393
Lazy Approximation for Solving Continuous Finite-Horizon MDPs
Solving Markov decision processes (MDPs) with continuous state spaces is a challenge due to, among other problems. the well-known curse of dimensionality. Nevertheless, numerous real-world applications such as transportation planning and telescope observation scheduling exhibit a critical dependence on continuous states. Current approaches to continuous-state MDPs include discretizing their transition models. In this paper, we propose and study an alternative, discretization-free approach we call lazy approximation. Empirical study shows that lazy approximation performs much better than discretization, and we successfully applied this new technique to a more realistic planetary rover planning problem.
623541
A multiresolution volume rendering framework for large-scale time-varying data visualization
We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a large-scale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a time-space partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve load-balanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.
28610395
A Path-Based Approach for Efficient Structural Join with Not-Predicates
There has been much research on XML query processing. However, there has been little work on the evaluation of XML queries involving not-predicates. Such queries are useful and common in many real-life applications. In this paper, we present a model called XQuery tree to model queries involving not-predicates and describe a path-based method to evaluate such queries efficiently. A comprehensive set of experiments is carried out to demonstrate the effectiveness and efficiency of the proposed solution.