Approximate range–sum query answering on data cubes with probabilistic guarantees

XML documents are often represented as DOM structures or trees. In some instances, due to the complexity of queries, XPath queries are better evaluated by traversing these structures rather than using summarization indexes. A requirement for the optimization of document navigation, is to efficiently decrease the number of traversed nodes. The optimization task is more difficult when the query framework allows a syntax that enlarges the search space, such as wildcards and descendant queries. To reduce the overhead of query processing, many database systems supporting XML rely on indexes. Such indexes are typically not able to adapt to memory restrictions or workload changes. A secondary data structure that uses little storage space and tunes itself to address hot spots in processing, can therefore be beneficial. In this paper, we propose a first method for creating, using, and maintaining selective signatures called hints to aid in the navigation of XML documents. Hints form a flexible data structure for pruning the search space, that can be used on its own or it can complement existing indexes. The amount of hints used is variable, and it depends on the storage limitation set a priori, and on the efficiency of the hints. Our experiments show that hints can improve the efficiency of navigational XML query processing by a large margin while using only little extra memory.

The management of spatial data in applications such as graphics and image processing, geography as well as computer aided design (CAD) imposes stringent new requirements on spatial database systems, in particular on efficient query processing of complex spatial objects. In this paper, we propose a two-level, multi-representation query processing technique which consists of a filter and a refinement level. The efficiency of spatial query processing is improved considerably using the following two design paradigms: first, divide and conquer, i.e. decomposition of complex spatial objects into more simple spatial components such as convex polygons, triangles or trapezoids, and second, application of efficient and robust spatial access methods for simple spatial objects. The most powerful ingredient in our approach is the concept of object decomposition. Applied to the refinement level of spatial query processing, it substitutes complex computational geometry algorithms by simple and fast algorithms for simple components. In this paper, we present four different decomposition techniques for polygonal shaped objects. The second part of the paper consists of an empirical performance comparison of those techniques using real and synthetic data sets. The four types of decomposition techniques are compared to each other and to the traditional approach with respect to the performance of spatial query processing. This comparison points out that our approach using object decomposition is superior to traditional query processing strategies.

Background. The digital transformation of dentistry involves not only the digitization of patient records but also the integration of intelligent Decision Support Systems (DSS), which can enhance diagnostic accuracy, standardize treatment protocols, and optimize clinician – patient interaction. With the rapid development of artificial intelligence (АІ) technologies, DSS are gaining increasing importance in both clinical and educational contexts within dentistry. Purpose – to systematize existing approaches to the classification, design, and implementation of DSS in dentistry, with a focus on their functional capabilities, architectural models, and preconditions for successful clinical deployment. Materials and Methods. The study is based on the analysis of peer-reviewed scientific literature published between 2020 and 2024 from Scopus, PubMed, and IEEE databases, as well as technical documentation of existing DSS solutions. Methods of systems analysis, descriptive classification, comparative evaluation, and synthesis of preconditions for implementation were applied. Two summary tables were developed: one on the advantages and limitations of different DSS types, and another on implementation preconditions. Results. Two primary classification axes of DSS were identified: functional (diagnostic, prognostic, therapeutic planning, administrative, educational, AI-based) and architectural (rule-based, case-based, AI-based, fuzzy logic, hybrid systems). Rule-based systems remain the most widely used due to their transparency and alignment with clinical protocols, while AI-driven systems demonstrate potential but require further validation and explainability. A set of key preconditions for effective implementation was formulated, including structured clinical data, interoperability with electronic health records, appropriate infrastructure, and clinical staff readiness. Conclusions. DSS has demonstrated high potential for improving the quality, safety, and personalization of dental care. Promising directions for future development include hybrid DSS models that combine rule-based and AI approaches, as well as the creation of unified standards for integration and legal compliance.

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., Signature R-tree or Signature UB-tree.

Prior research in design indicates that design considerations (as reflected through designers’ decisions) can greatly differ from users’ viewpoints when interacting with technologies. This is typically evidenced within designers’ proposals to focus on utility and appearance whilst users’ viewpoints (or reactions) to technologies can take multiple forms (e.g. visceral, behavioral and reflective). Heuristics are simple 'rules of thumb' applied in designing technologies, focusing on design principles that aim to satisfy utility and appearance while providing a means of ‘standardization’ for designers; the same applies to usability heuristics, which are employed at the end of the design process, to assess the usability of designed systems, both in terms of usability and aesthetics. Whilst design and evaluation heuristics can offer a level of ‘standardization’, users’ viewpoints, reactions and interpretation of the designed technology can be diverse (i.e. their emotional and behavioral responses can greatly differ, making it difficult to ‘standardize’). There are several reasons why this is happening, e.g. from individual differences to context and domain-based reasons; the latter is, indeed, one of the common issues in heuristics-based design and usability heuristics i.e. these are domain-specific. It appears that there is limited research in attempting to understand the relationship and mapping between users’ own behavior (i.e. behavior-based heuristics) and design heuristics. An open question is to what extent the ‘expertise’ and empirical knowledge of designers can overlook any intuitive or unconscious behavior-oriented heuristics that manifest 'discreetly' and 'automatically' when designing technologies.The present paper is drawing data from two different user studies (route optimization problem solving and data analytics), aiming to explore what types of behavior-based heuristics people employ when performing data analytic tasks and problem-solving tasks (without the use of computer interfaces). These two different domains are chosen as they contain different requirements (i.e. completion of healthcare data analytic tasks versus completion of visuospatial mathematical optimization problems), thus providing a diverse enough comparison to explore user behaviors within domains that require some form of computer interface. By identifying behavioral patterns and heuristics people employ when engaging with these two different domains, we are able to identify interactional needs that can inform the design of novel user interfaces (i.e. decision-support systems) that can assist in the completion of complex tasks, instead of designing technologies first and then applying design heuristics to adjust and evaluate the designs. Observation methods, problem-based/scenario-based approaches and Think Aloud protocols have been employed to identify heuristics in both studies while overall performance (in terms of task completion and accuracy) has also been measured. The research found that behavior-based heuristics can be task-based and problem-based and can manifest early in design thus need capturing at user requirements elicitation. Such behavior-based heuristics can be interactional, systemic, cognitive and experiential and need to be considered alongside usability heuristics. There is a need to formalize experiences against tasks, especially as ‘smart’ technologies and multi-systemic data exchange can be stakeholders and ‘end-users’. A conceptual design verification framework is proposed to enhance design and evaluation processes.

Several recently proposed randomized testing tools for concurrent and distributed systems come with theoretical guarantees on their success. The key to these guarantees is a notion of bug depth—the minimum length of a sequence of events sufficient to expose the bug—and a characterization of d -hitting families of schedules—a set of schedules guaranteed to cover every bug of given depth d . Previous results show that in certain cases the size of a d -hitting family can be significantly smaller than the total number of possible schedules. However, these results either assume shared-memory multithreading, or that the underlying partial ordering of events is known statically and has special structure. These assumptions are not met by distributed message-passing applications. In this paper, we present a randomized scheduling algorithm for testing distributed systems. In contrast to previous approaches, our algorithm works for arbitrary partially ordered sets of events revealed online as the program is being executed. We show that for partial orders of width at most w and size at most n (both statically unknown), our algorithm is guaranteed to sample from at most w 2 n d −1 schedules, for every fixed bug depth d . Thus, our algorithm discovers a bug of depth d with probability at least 1 / ( w 2 n d −1 ). As a special case, our algorithm recovers a previous randomized testing algorithm for multithreaded programs. Our algorithm is simple to implement, but the correctness arguments depend on difficult combinatorial results about online dimension and online chain partitioning of partially ordered sets. We have implemented our algorithm in a randomized testing tool for distributed message-passing programs. We show that our algorithm can find bugs in distributed systems such as Zookeeper and Cassandra, and empirically outperforms naive random exploration while providing theoretical guarantees.

Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases

The efficiency of spatial query processing is crucial for many applications such as location-based services. In spatial networks, queries like k-NN queries are all based on network distance evaluation. Classic solutions for these queries rely on network expansion and are not efficient enough for large networks. Some approaches have improved the query efficiency but brought considerable space cost for index. To address these problems, we propose a hierarchical graph partitioning based index named Partition Tree. It organizes the vertices of a spatial network into a hierarchy through a series of graph partitioning processes. Meanwhile precomputed distances are associated with this hierarchy to facilitate efficient query processing. Inspired by the observation that queries are usually invoked around objects of interest, we propose a query-oriented optimization on top of the Partition Tree. It uses a cost model to evaluate the influence of the object distribution and partitioning topology on the query efficiency. Then a cost-efficient graph partitioning method is developed based on this cost model. Experimental results on real datasets demonstrate that our proposed index and algorithms have superior performance over the state-of-the-art approaches and are scalable to large spatial networks.

In this paper we propose a method for efficient management of large spatial ontologies. Current spatial ontologies are usually represented using an ontology language, such as OWL and stored as OWL files. However, we have observed some shortcomings using this approach especially in the efficiency of spatial query processing. This fact motivated the development of a hybrid approach that uses an R-tree as a spatial index structure. In this way we are able to support efficient query processing over large spatial ontologies, maintaining the benefits of ontological reasoning. We present a case study for emergency teams during Search and Rescue (SaR) operations showing how an Ontology Data Service (SHARE-ODS) can benefit from a spatial index. Performance evaluation shows the superiority of our proposed technique compared to the original approach. To the best of our knowledge, this is the first attempt to address the problem of efficient management of large spatial ontology bases.

Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Existing Locality Sensitive Hashing (LSH) techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the query object. This leads to wasted and redundant work due to two main reasons: 1) not all feature vectors may contribute equally in finding the top-k similar multimedia objects, and 2) feature vectors are treated independently during query processing. Additionally, there is no theoretical guarantee on the returned multimedia results. In this work, we propose a practical and efficient indexing approach for finding top-k approximate nearest neighbors for multimedia data using LSH called mmLSH, which can provide theoretical guarantees on the returned multimedia results. Additionally, we present a buffer-conscious strategy to speed up the query processing. Experimental evaluation shows significant gains in performance time and accuracy for different real multimedia datasets when compared against state-of-the-art LSH techniques.

Robust multiple-instance learning ensembles using random subspace instance selection

Context. The development of effective distance metrics and similarity measures for categorical features is an important task in data analysis, machine learning, and decision theory since a significant portion of object properties is described by non-numerical values. Typically, the dependence between categorical features may be more complex than simply comparing them for equality or inequality. Such attributes can be relatively similar, and to construct an effective model, it is necessary to consider this similarity when calculating distance or similarity measures.
 Objective. The aim of the study is to improve the efficiency of solving practical data analysis problems by developing mathematical tools for determining the similarity of objects based on categorical ordered features.
 Method. A distance based on weighted Manhattan distance and a similarity measure for determining the similarity of objects based on categorical ordinal features (i.e. a linear order with scales of preference considering the problem domain can be specified on the attribute value set) are proposed. It is proven that the distance formula satisfies the axioms of non-negativity, symmetry, triangle inequality, and upper bound, and therefore is a distance metric in the space of ranked categorical features. It is also proven that the similarity measure presented in the study satisfies the axioms of boundedness, symmetry, maximum and minimum similarity, and is described by a decreasing function.
 Results. The developed approach has been implemented in an applied problem of determining the degree of similarity between objects described by ordered categorical features.
 Conclusions. In this study, mathematical tools were developed to determine similarity between structured data described by categorical attributes that can be ordered based on a specific priority in the form of a ranking system with preferences. Their properties were analyzed. Experimental studies have shown the convenience and “intuitive understanding” of the logic of data processing in solving practical problems. The proposed approach can provide the opportunity to conduct new meaningful research in data analysis. Prospects for further research lie in the experimental use of the proposed tools in practical tasks and in studying their effectiveness.

The problem of creating a decision support system (DSS) for products/services in IT companies using an effective conceptual approach has been solved. The developed mathematical model of DSS for IT-business is represented. It-business is considered as a business focused on implementation and support of innovative projects. The mathematical model is intended for the use by an IT company as a separate IT project and a group of IT projects. Elements of graph theory and tensor analysis were used to create the mathematical model. DSS, with the use of the mentioned model, can be interactive and offer an effective solution by minimizing or maximizing the factor or group of factors that influence that decision. The search for the best DSS solution was reduced to the task of optimizing the integrated criterion, minimizing or maximizing the factor or group of factors influencing the decision. Since the possibility of negative values of the objective function for certain parameters of the weight of the edge of the digraph was clarified, the application of the Bellman – Ford algorithm for this reason was justified. The method of linear convolution was used for efficient operation of the algorithm. This allowed us to use the algorithm to solve multicriteria problems, in the absence of information about the form of the integral function. An adaptive algorithm was used to speed up the work. It is that the results of query processing form a database of templates. This will allow you to quickly find a solution for a similar query, even if you change the initial conditions. A universal approach to structuring the work of an IT-company has been developed. This allows formalizing requests to the DSS and to receive formalized responses both at the specific stage of the project, a separate type of work on the project and the project portfolio and the IT-company as a whole. This, in turn, allows optimizing the use of computer resources when working with DSS, reducing both the time to perform requests in the interactive mode, and for the formation of variants of the solutions for the tasks set by the user via DSS. Structuring according to the main features of the project allowed group the work of the IT company according to available resources and desired results. This will allow you to plan and organize the work of each employee to work not only in one project, but to perform work in different projects. It is also possible to plan and organize the work of working groups. Each of the executors of these groups is formally subordinated to their structural units and, at the same time, to the coordinator of the respective project. The proposed approach allows you quickly reorienting DSS to perform radically different tasks, to fulfill a different purpose, to other areas of work of an IT company, other projects. It would only take time to update and replenish the DSS database

This work studies the problem of 2-dimensional searching for the 3-sided range query of the form [a, b] x (-∞, c] in both main and external memory, by considering a variety of input distributions. A dynamic linear main memory solution is proposed, which answers 3-sided queries in O(log n + t) worst case time and scales with O (log log n) expected with high probability update time, under continuous μ-random distributions of the x and y coordinates, where n is the current number of stored points and t is the size of the query output. Our expected update bound constitutes a considerable improvement over the O(log n) update time bound achieved by the classic Priority Search Tree of McCreight [23], as well as over the Fusion Priority Search Tree of Willard [30], which requires O(log n/log log n) time for all operations. Moreover, we externalize this solution, gaining O(logBn + t/B) worst case and O(logBlogn) amortized expected with high probability I/Os for query and update operations respectively, where B is the disk block size. Then, combining the Modified Priority Search Tree [27] with the Priority Search Tree [23], we achieve a query time of O(log log n + t) expected with high probability and an update time of O(log log n) expected with high probability, under the assumption that the x-coordinates are continuously drawn from a smooth distribution and the y-coordinates are continuously drawn from a more restricted class of distributions. The total space is linear. Finally, we externalize this solution, obtaining a dynamic data structure that answers 3-sided queries in O(logB log n + t/B) I/Os expected with high probability, and it can be updated in O(logB log n) I/Os amortized expected with high probability and consumes O(n/B) space, under the same assumptions.

Efficient external memory structures for range-aggregate queries