New Generation Computing

Prolog-ELF kết hợp logic mờ và một số hàm hữu ích vào Prolog đã được triển khai như một ngôn ngữ cơ bản để xây dựng các hệ thống tri thức có sự không chắc chắn hoặc độ mơ hồ. Prolog-ELF kế thừa tất cả các đặc điểm cơ bản mong muốn của Prolog. Ngoài các khẳng định với giá trị chân lý giữa 1.0 và 0.5 (0 cho các trường hợp đặc biệt), các tập mờ có thể được thao tác rất dễ dàng. Một ứng dụng của cơ sở dữ liệu logic mờ được minh họa.

A huge amount of data is daily collected from clinical microbiology laboratories. These data concern the resistance or susceptibility of bacteria to tested antibiotics. Almost all microbiology laboratories follow standard antibiotic testing guidelines which suggest antibiotic test execution methods and result interpretation and validation (among them, those annually published by NCCLS2,3). Guidelines basically specify, for each species, the antibiotics to be tested, how to interpret the results of tests and a list of exceptions regarding particular antibiotic test results. Even if these standards are quite assessed, they do not consider peculiar features of a given hospital laboratory, which possibly influence the antimicrobial test results, and the further validation process. In order to improve and better tailor the validation process, we have applied knowledge discovery techniques, and data mining in particular, to microbiological data with the purpose of discovering new validation rules, not yet included in NCCLS guidelines, but considered plausible and correct by interviewed experts. In particular, we applied the knowledge discovery process in order to find (association) rules relating to each other the susceptibility or resistance of a bacterium to different antibiotics. This approach is not antithetic, but complementary to that based on NCCLS rules: it proved very effective in validating some of them, and also in extending that compendium. In this respect, the new discovered knowledge has lead microbiologists to be aware of new correlations among some antimicrobial test results, which were previously unnoticed. Last but not least, the new discovered rules, taking into account the history of the considered laboratory, are better tailored to the hospital situation, and this is very important since some resistances to antibiotics are specific to particular, local hospital environments.

This paper presents the performance analysis results for the RAP-WAM AND-Parallel Prolog architecture on shared-memory multiprocessor organizations. The goal of this parallel model is to provide inference speeds beyond those attainable in sequential systems, while supporting conventional logic programming semantics. Special emphasis is placed on sequential performance, storage efficiency, and low control overhead. First, the concepts and techniques used in the parallel execution model are described, along with the general methodology, benchmarks, and simulation tools used for its evaluation. Results are given both at the memory reference level and at the memory organization level. A two-level shared-memory architecture model is presented together with an analysis of various solutions to the cache coherency problem. Finally, RAP-WAM shared-memory simulation results are presented. It is argued that the RAP-WAM model can exploit coherent caches and attain speeds in excess of 2 MLIPS with current shared-memory multiprocessing technology for real applications that exhibit medium degrees of parallelism.

ODM is a new data model thatintegrates the features of object oriented programming languages (e.g. Smalltalk-80) and Relational Data Model (RDM). It extends the data structures and operations of RDM and also provides the features of object oriented programming such as improved semantics, data abstraction, reusability of data structures and codes, and extensibility. We have introduced the concept of ‘u-set’ (uniform set) as an extension of relation of RDM. We employ messages to define an extension of RDM attributes and tuples. Definition of classes for databases, u-sets, and tuples allows us to define new (or modify existing) operations for the databases, u-sets, or tuples. Each database and its elements are u-sets. Au-set is a set of elements ‘conformable’ to a fixed class, namely the ‘base class’ of the u-set. This roughly means that all elements of a u-set support the operations defined in its base class, but they need not have identical data structures. The base class may be any class, the elements of a u-set are not necessarily tuples. This provides an arbitrary deep hierarchy of tuples, u-sets and databases. In particular set-valued, tuple-valued, and derived attributes are supported, and an element of a u-set may be another u-set.

In the recent investigations of reducing the relational join operation complexity several hash-based partitioned-join stategies have been introduced. All of these strategies depend upon the costly operation of data space partitioning before the join can be carried out. We had previously introduced a partitioned-join based on a dynamic and order preserving multidimensional data organization called DYOP. The present study extends the earlier research on DYOP and constructs a simulation model. The simulation studies on DYOP and subsequent comparisons of all the partitioned-join methodologies including DYOP have proven that space utilization of DYOP improves with the increasing number of attributes. Furthermore, the DYOP based join outperforms all the hash-based methodologies by greatly reducing the total I/O bandwidth required for the entire partitioned-join operation. The comparison model is independent of the architectural issues such as multiprocessing, multiple disk usage, and large memory availability all of which help to further increase the efficiency of the operation.

Condition-based maintenance (CBM) recommends maintenance actions based on the information collected through condition monitoring. In many modern cars, the condition of each subsystem can be monitored by onboard vehicle telematics systems. Prognostics is an important aspect in a CBM program as it deals with prediction of future faults. In this paper, we present a data mining approach to prognosis of vehicle failures. A multitarget probability estimation algorithm (M-IFN) is applied to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The results of the multi-target algorithm are shown to be superior to a singletarget probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis.

This paper extends Reiter’s closed world assumption to cases where the assumption is applied in a precedence order between predicates. The extended assumptions are: thepartial closed world assumption, thehierarchical closed world assumption and thestepwise closed world assumption. The paper also defines an extension of Horn formulas and shows several consistency results about the theory obtained from the extended Horn formulas by applying the proposed assumptions. In particular, the paper shows that both the hierarchical closed world assumption and the stepwise closed world assumption characterize the perfect model of stratified programs.

We propose algorithms to perform operations concurrently on treaps in a shared memory multi-core environment. Concurrent treaps hold the advantage of using nodes’ priority for maintaining the height of the treaps. To achieve synchronization, concurrent treaps make use of fine-grained locking mechanism along with logical ordering and physical ordering of nodes’ keys. We initially study the throughput and performance-per-Watt (PPW) of our concurrent treap implementation and observe that it scales well, and performs better than the state-of-the-art implementations. We further continue studies to understand the impact of different locking objects on both throughput and PPW. Our experiments show that a concurrent treap implementation that uses AtomicInteger as the locking object provides better throughput and PPW, at the same time uses a low memory footprint. As part of the application study, we consider concurrent interval trees by choosing different underlying concurrent search tree implementations as the base. We observe that the concurrent interval tree implementation that uses concurrent treap as an underlying data structure provides better throughput.