Distance-based Join Queries (DJQs) have multiple applications in spatial databases, Geographic Information Systems, and other areas. The K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ) are well-known DJQs that have been widely studied and can be solved using plane-sweep techniques, which are efficient but must keep the whole datasets in main memory. In this work, we propose DJQ algorithms that work with data represented using a k2-tree, a compact data structure for binary grids. Our algorithms solve KCPQ and εDJQ queries, as well as several window-constrained variants, taking advantage of the indexing capabilities of k2-trees to efficiently answer queries without the need to decompress the data. Our experimental evaluation with large datasets shows that k2-tree algorithms are up to 5 times faster than plane-sweep algorithms in KCPQ, and 5–30 times faster in εDJQ. In variants that are window-constrained, our algorithms are competitive in most scenarios and faster for large windows. Additionally, our algorithms are not very affected by the distribution of the data and yield much more predictable query times, showing up to 30 times smaller variance in query times than plane sweep, depending on the location of the query window.

Abstract Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS.

Abstract The W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

The Internet of Things (IoT) is composed of many heterogeneous pieces. The Web of Things (WoT) was created to establish a common device definition language, the Thing Description (TD), that would allow developers to set an interaction mechanism without understanding the different device implementations. Besides a definition language, a WoT ecosystem must be characterized by a robust discovery system in which the other Thing Descriptions of the devices can be easily located, registered and consulted. According to these two pillars (description and discovering), this article presents a Discovery Service model for the WoT, capable of working proactively, searching for IoT devices compatible with the Thing Description, and passively being able to register, query or list different devices when the client chooses. Furthermore, Digital Dice is used in the proposal, a software abstraction of IoT devices for managing devices, adding some features and taking advantage of microservices architectures. Finally, in the recent updates and modifications over the WoT recommendations, one of the main problems researchers may face is that there are few public domain implementations. This article also presents a fully interactive solution using the Discovery Service and Digital Dice to address this problem.

The communication between different IoT systems presents a series of challenges in the processing and analysis of the heterogeneous information managed by said systems. The W3C Web of Things (WoT) standard offers a common model to define those devices following the guidelines established by the Thing Description. One of the caveats of the WoT is that the Thing Description does not define behaviour, so there is no way to know if an interaction is the consequence of another one. Each IoT device is represented using a Thing Description as an individual entity by design. In this paper, we propose a mechanism to improve interoperability between devices, laying the foundations on how to establish a relation linking them. This relation is formed by two different entities: the "causes" and the "effects". These two entities are at the same time interactions affordances provided by the Thing Description schema of each thing involved. Cause-effect relations help to define how devices work with each other following an event communication strategy.

The Web of Things proposes an IoT device definition framework, the Thing Description. Conceptually, this definition covers a wide range of devices, protocols, and data types. On the other hand, the proliferation of IoT devices and the traits that they usually exhibit, such as limited number of resources, leads to the emergence of bottlenecks that cause the slowdown of requests and even the shutdown of devices. To solve these problems, we introduce the concept of Digital Dice, a digital representation of an IoT device based on microservices that uses the framework established by the W3C Web of Things. This article describes WoTnectivity, a mechanism that allows the connection of multiple protocols on IoT devices by means of several common communication patterns. Besides, establishing the possibility of connecting with different protocols, WoTnectivity has been developed with future expansion in mind, thus an explanation on how to add more protocols is also studied in this paper.

The k Nearest-Neighbor (k-NN) query is a common spatial query that appears in several big data applications. Typically, GPU devices have much larger numbers of processing cores than CPUs and faster device memory than main memory accessed by CPUs, thus, providing higher computing power. We propose and implement a new GPU-based partitioning algorithm for the k-NN query, using the CUDA runtime API. Due to partitioning, this algorithm avoids calculating distances for the whole dataset. Using synthetic and real datasets, we present an extensive experimental performance comparison against six existing algorithms. These algorithms are based on calculating distances for the whole in-memory dataset. This comparison shows that the new algorithm excels in all the conducted experiments and outperforms these six algorithms.

Spatial access methods have been extensively studied in the literature, during last decades. Access methods were designed for efficient processing of demanding queries and extensive comparisons between such methods have been presented. However, choosing the best values for the parameters that affect the performance of a spatial access method when such a method is expected to be utilized within a specific workload / context of operations has not been studied, so far. In this paper, we present the design and implementation of a framework to evaluate and optimize a spatial index. The (very popular) family of R-trees is chosen as the index of focus, though the same process can be applied for other (spatial, or not) indexes or combinations of them. We elaborate on the antagonizing aspects in the design of an R-tree, present the design and implementation of a benchmarking framework and develop a performance model for this index that incorporates benchmarking results. Next, we develop an optimization framework that uses this model to provide an optimized set up (node occupancies and node splitting method) for a specific use context (dataset type, tree size, number of queries and type of queries). We also present experimental results of an indicative use of the developed benchmarking framework and optimizer for a limited range of use contexts.

In modern database applications the similarity or dissimilarity of complex objects is examined by performing distance-based queries (DBQs) on data of high dimensionality. The R-tree and its variations are commonly cited multidimensional access methods that can beused for answering such queries. Although the related algorithms work well for low-dimensional data spaces, their performance degrades as the number of dimensions increases (dimensionality curse). In order to obtain acceptable response time in high-dimensional data spaces, algorithms that obtain approximate solutions can be used. Approximation techniques, like N-consider (based on the tree structure), α-allowance and ε-approximate (based on distance), or Time-consider (based on time) can be applied in branch-and-bound algorithms for DBQs inorder to control the trade-off between cost and accuracy of the result. In this paper, we improve previous approximate DBQ algorithms by applying a combination of the approximation techniques in the same query algorithm (hybrid approximation scheme). We investigate the performance of these improvements for one of the most representative DBQs (the K-closest pairs query, K-CPQ) in high-dimensional data spaces, as well as the influence of the algorithmic parameters on the control of the trade-off between the response time and the accuracy of the result. The outcome of the experimental evaluation, using synthetic and real datasets, is the derivation of the outperforming DBQ approximate algorithm for large high-dimensional point datasets.

This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the R-tree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is treated as a special case. An extensive study, based on experiments performed with synthetic as well as with real point data sets, is presented. A wide range of values for the basic parameters affecting the performance of the algorithms, especially the effect of overlap between the two data sets, is explored. Moreover, an algorithmic as well as an experimental comparison with existing incremental algorithms addressing the same problem is presented. In most settings, the new algorithms proposed clearly outperform the existing ones.