Design Considerations for a Prolog Database Engine

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Due to the huge scale and the number of components, big data is difficult to work with the use of relational databases and desktop statistics and visualization package. Much database replication technology is used to increase the MTTF, but few have a large database system, the traditional method of backup is not feasible, expensive manpower costs reduce MTTR. On the basis of analyzing the characteristics of data in large databases, we propose a new method called Detaching Read-Only (DRO) mechanism and its changes DRO+. It reduces MTTR by reducing the physical change of the data in each database, by separating data node size granularity. Analysis and experimental results show that our method can not only reduce the MTTR an order of magnitude, but there is no additional hardware costs, but also reduce the high human costs.

There is a growing interest in the application of Prolog and related languages like Parlog to database implementation. Existing Prolog systems, however, are unable to cope with large sets of data of the size necessary for databases. A large Prolog database would consist of millions of clauses. The size of physical memory required to store the database has deemed main memory impractical in many computer systems. Therefore clauses are usually placed on secondary disc memory. Clause retrieval, however, becomes extremely slow. Furthermore, the enormous amount of time required for unification upon a large set of clauses has drastically degraded system performance. Either of these factors leads to non-timely database response which is irritating and in many cases, intolerable. To mediate these problems, a special hardware unit is proposed - the CLAuse Retrieval Engine (CLARE). The functions of the CLARE are to search discs storing database clauses on-the-fly and select potentially unifiable clauses. Two stages of filtering will be applied to the database clauses; thus a small resolution set will be guaranteed.

Earlier a new rough set theory for very large databases was proposed by T.Y. Lin (1996). In this paper the authors attempt to evaluate the performance of such a rough set theory for a very large database. ORACLE, a relational database management system (RDBMS), is the market leader in open system databases. Windows NT has been growing into the data server environment and is a strong contender for decision support system applications with its open and cost-effective architecture. So ORACLE running under Windows NT was used in this report. The main goal of this research is to formulate a suitable rough set theory for very large databases.

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.

The purpose of this article was to discuss common issues associated with large databases and present possible solutions to improve the quality and usefulness of large database research. The volume of electronic healthcare-related data is growing exponentially. Some of these data are being stored in registries and administrative databases. These data repositories are increasingly common and can serve as sources of nurse-driven research and quality improvement activities. Although these large databases have a wealth of useful information, they have limitations that may bias results. These include missing data and cases, data accuracy and validity, and the statistical effect of large samples. Researchers using large databases to address quality, safety, clinical, or systems issues have a variety of available techniques to deal with data issues. Proper data cleaning activities such as screening, visualization, and outlier/inlier identification are essential for addressing inaccurate values within large data sets. Common methods for addressing missing data include case analyses and various imputation techniques. Statistical approaches such as risk reductions and effect size are also useful when working with large sample sizes. Registries and administrative databases provide healthcare researchers with increasing opportunities to address a wide variety of important practice and patient care questions. Healthcare researchers are encouraged to explore large data sets as they look for ways to improve patient safety and quality care, develop evidence-based practice guidelines, and fulfill regulatory and accreditation requirements.

Interactive problem solving involves user-machine dialogues to solve unstructured problems, such as selecting marketing strategies and planning mass transit routes. A characteristic of most interactive problem solving systems is that they operate on data bases that are special purpose subsets derived from a large data base. The problem solving system must include code for interfacing with this data base, or the data must be converted to the formats required by the problem solving system. Effective interactive problem solving requires a data management system which provides flexibility in accessing a large data base and fast performance for the problem solving system. This paper describes a method, called data extraction, for interfacing large data bases with interactive problem solving systems. Figure 1 shows the basic components in the interface. A large data base management system is used to maintain and protect a set of data files. Data extraction is used to aggregate and subset from this set to provide information for problem solving. In addition to an I/O interface, data extraction provides interactive data description and presentation functions. For more details on the functional requirements of the data extraction components see [2].

Shows that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits. It is also shown that the performance of these systems scale consistently with the size of the training database, where the error rate is cut by more than half for every tenfold increase in the size of the training set from 10 to 100,000 examples. Three distance metrics for the standard nearest neighbor classification system are investigated: a simple Hamming distance metric, a pixel distance metric, and a metric based on the extraction of penstroke features. Systems employing these metrics were trained and tested on a standard, publicly available, database of nearly 225,000 digits provided by the National Institute of Standards and Technology. Additionally, a confidence metric is both introduced by the authors and also discovered and optimized by the system. The new confidence measure proves to be superior to the commonly used nearest neighbor distance. >

In this article, we evaluate the effectiveness of a pre-classification scheme for the fast retrieval of faces in a large image database. The studied approach is based on a partitioning of the face space through a clustering of face images. Mainly two issues are discussed. How to perform clustering with a non-trivial probabilistic measure of similarity between faces? How to assign face images to all clusters probabilistically to form a robust characterization vector? It is shown experimentally on the FERET face database that, with this simple approach, the cost of a search can be reduced by a factor 6 or 7 with no significant degradation of the performance.

Tables constitute the majority of structured data in enterprise environments. While single-table question answering has received significant attention, research on multi-table reasoning remains limited. Compared to single-table QA, multi-table QA requires schema alignment, relational inference, and scalable context man- agement. We introduce tool-augmented reasoning as a paradigm for multi-table QA, and systematically study two complementary strategies: (1) free-form tool interaction, where models iteratively call exploration and computation tools, and(2) structured agent workflows, which stage tool-use into exploration, preparation, and analysis phases. Using the TQA-Bench dataset, we evaluate GPT-4o-mini and GPT-5-mini across both small (8k) and large (128k) database scales under varying tool context constraints. We show that tool augmentation substantially improves robustness and accuracy over direct prompting, with gains up to +28 percentage points. Structured workflows yield further benefits for weaker models on large databases, but regress for stronger models, revealing a scale–capacity trade-off in the value of structure. These results establish tool-augmented reasoning as a powerful paradigm for multi-table QA. Structure aids weaker models under scale but constrains stronger models, underscoring that tool-use strategies must be adapted jointly to model ability and database complexity.

Video cameras are widely deployed along city streets, interstate highways, traffic lights, stop signs and toll booths by entities that perform traffic monitoring and law enforcement. The videos captured by these cameras are typically compressed and stored in large databases. Performing a rapid search for a specific vehicle within a large database of compressed videos is often required and can be a time-critical life or death situation. In this paper, we propose video compression and decompression algorithms that enable fast and efficient vehicle or, more generally, event searches in large video databases. The proposed algorithm selects reference frames (i.e., I-frames) based on a vehicle having been detected at a specified position within the scene being monitored while compressing a video sequence. A search for a specific vehicle in the compressed video stream is performed across the reference frames only, which does not require decompression of the full video sequence as in traditional search algorithms. Our experimental results on videos captured in a local road show that the proposed algorithm significantly reduces the search space (thus reducing time and computational resources) in vehicle search tasks within compressed video streams, particularly those captured in light traffic volume conditions.

Large human motion databases contain variants of natural motions that are valuable for animation generation and synthesis. But retrieving visually similar motions is still a difficult and time-consuming problem. This paper provides methods for identifying visually and numerically similar motions in a large database given a query of motion segment. We propose an efficient indexing strategy that represents the motions compactly through a preprocessing. This representation scales down the range of searching the database. Motions in this range are possible candidates of the final matches. For detailed comparisons between the query and the candidates, we propose an algorithm that compares the motions’ curves swiftly. Our methods can apply to large human motion databases and achieve high performance and accuracy compared with previous work. We present experimental results on testing a database of about 2.9 million frames, or about 27 hours of motions played at 30 Hz.

Processing large databases is intricate for databases involving several types of textures. In particular, for content-based image retrieval, a query has to be compared with all the samples pertaining to the database in order to identify its content/class and this is time consuming. Furthermore, modeling of a large database is a difficult task for databases involving several types of textures since accurate models for certain textures are not guaranteed to be very relevant for other types of textures and vice-versa. In order to save computational time and increase performance in processing of large texture databases, the present paper proposes structuring texture databases by using stochasticity metaclasses.

A method is proposed, and shown to be able to find active compounds in a Large Database by iterative selection/screening cycles. The method is derived from genetic algorithms. Starting from an initial small parent population the next compounds to screen are selected by similarity search based on the structural features of two high rating parents. The performance of the method is demonstrated using 20000 compounds with biological data from the NCI1 database.

Mining of frequent patterns has been studied popularly in data mining area. However, very little work has been done on the problem of updating mined patterns upon threshold changes, in spite of its practical benefits. When users interactively mine frequent patterns, one difficulty is how to select an appropriate minimum support threshold. So, it is often the case that they have to continuously tune the threshold. A direct way is to re-execute the mining procedure many times with varied thresholds, which is nontrivial in large database. In this paper, an efficient Extension and Re-mining algorithm is proposed for update of previously discovered frequent patterns upon threshold changes. The algorithm proposed in this paper has been implemented and its performance is compared with re-running FP-growth algorithm under different thresholds. The study shows that our algorithm is significantly faster than the latter, especially when mining long frequent patterns in large databases.