Efficient Learned Spatial Index With Interpolation Function Based Learned Model

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Adaptive online extreme learning machine by regulating forgetting factor by concept drift map

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

The endpoint control of Basic Oxygen Furnace (BOF) steelmaking depends on the prediction of the endpoint carbon content and temperature. However, predicting these variables is challenging because of the numerous working conditions in the industrial field and the volatility of the sensor data collected during BOF steelmaking. The accuracy of prediction models in ensemble learning depends significantly on the initial distribution of data. However, the complex nature of BOF steelmaking data makes it challenging to generate diverse subsets, which ultimately affects the accuracy of predictions. This paper presents a new approach called Graph Convolutional Network Node Embedding Supervised Ensemble Clustering (GESupEC) for soft sensor modelling in ensemble learning to tackle these issues. GESupEC utilizes a similarity graph derived from a co-association matrix and employs graph convolutional networks to extract structural information among nodes. By optimising the clustering loss within the network, GESupEC learns compact node representations that are useful for the clustering task. Furthermore, it generates a reconstruction matrix based on the similarity of node embeddings. This matrix helps with the extraction of a suitable subset of data for BOF steelmaking through matrix decomposition. After that, the gradient boosting decision tree regression sub-model is established based on the data subset. An ensemble strategy called Gray Relational Analysis Weighted Average is proposed, which assigns weights based on the grey relation similarity between test samples and different data subsets. This weighted average strategy aims to enhance the accuracy of carbon content and temperature predictions. When tested with actual BOF steelmaking generation process data, the prediction accuracy of carbon content reached 88.6% within the error range of ±0.02%, and the prediction accuracy of temperature reached 92.6% within the error range of ±10 °C.

Nowadays, flash memory is widely used in various portable devices such as PDAs (personal digital assistants) and HPCs (handheld PCs). With the development of location-based services (LBS) provided by those portable devices, how to provide efficient spatial data management over flash-equipped LBS devices has become a critical issue in recent years. In this paper, we aim to propose an efficient flash-optimized spatial tree index, called OR-tree, to improve the queries and updates on flash-resident spatial data. The OR-tree is based on R-tree and introduces two new techniques to make the R-tree suitable for flash memory storage system. First, we employ a lazy node splitting policy by introducing overflow nodes for the leaf nodes in R-tree. Next, we redesign the algorithms of update operations to adapt the unbalanced OR-tree structure. To evaluate the performance of the OR-tree, we conduct experiments on a spatial data set gathered from the R-tree portal. We run different workloads and compare the OR-tree with other competitors on the basis of different metrics including flash page usage, flash write count, erasure count, and runtime. The experimental results show that the OR-tree outperforms the competitors considering both space efficiency and time performance.

This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary all-encompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user???s area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.

Spatial index is the foundation of spatial database, while efficiency improvement of traditional serial spatial index has nearly reached its limit, it is therefore necessary to develop parallel spatial index approaches to break the bottleneck in accessing the root node in serial mode. This paper proposes a parallel spatial index called Hilbert R tree index, which can be carried on multicore CPU and computer cluster for parallel spatial queries and data retrieval. This new index method utilizes BIRCH clustering algorithm for spatial classification and data partition before distributed data deployment, considering geographic data characteristics; and creates spatial index in parallel environment by using of Hilbert filling curve. The test results demonstrate that parallel Hilbert R tree index based on BIRCH clustering algorithm can not only maintain internal spatial relations and the attributes of geographic dataset, but also has efficient performance in partitioning and retrieving spatial data.

Adaptive indexing allows for the progressive and simultaneous query-driven exploration and indexing of memoryresident data, starting as soon as they become available without upfront indexing. This technique has been so far applied to onedimensional and multidimensional data, as well as to objects with spatial extent arising in geographic information systems. However, existing spatial adaptive indexing methods cater to static data made available in an one-off manner. To date, no spatial adaptive indexing method can ingest data updates interleaved with data exploration. In this paper we introduce GLIDE, a novel method that intertwines the adaptive indexing and incremental updating of a spatial-object data set. GLIDE builds a hierarchical spatial index incrementally in response to queries and also ingests updates judiciously into it. We examine several design choices and settle for a variant that combines gradual self-driven top-down insertions with query-driven indexing operations. In an extensive experimental comparison, we show that GLIDE achieves a lower cumulative cost than upfront-indexing methods and adaptiveindexing baselines.

Geographic Information Systems (GIS) are increasingly managing very large sets of data and hence a centralized data repository may not always provide the most scalable solution. Here we introduce a novel approach to manage spatial data by leveraging structured Peer-to-Peer (P2P) systems based on Distributed Hash Tables (DHTs). DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and as a result they are extremely scalable. Furthermore, the adoption of uniform hash functions ensures excellent load balancing. However, range queries -- which are very common with spatial data -- cannot be executed efficiently because the hash functions unfortunately destroy any existing data locality. Here we report on the design of an Adaptive Spatial Peer-to-pEer Network (ASPEN) that extends Content Addressable Networks (CAN) to preserve spatial locality information while also retaining many of the load balancing properties of DHT systems. We introduce the concept of scatter regions, which are spatial data distribution units that optimize both load balancing and spatial range query processing at the same time. We present a data object key generation function and algorithms for spatial range queries. We rigorously evaluate our technique with both synthetic and real world data sets and the results demonstrate the efficient execution of spatial range queries in the ASPEN system.

The key challenge for Dynamic Data Driven Applications Systems (DDDAS) operating in time-varying environments is to predict when the learned model may lose relevance. If the learned model loses relevance, then the autonomous system is at risk of making wrong decisions. The Entropic Value at Risk (EVAR) is a computationally efficient and coherent risk measure that can be utilized to quantify this model relevance. The value of EVAR is calculated with respect to an assumed confidence value; e.g., a 90% confidence may be desired for robust decision-making. Without a model on the confidence value directly, there is no guarantee that EVAR calculations will reflect the uncertainty present in dynamic real-world environments. In this paper, we present a Bayesian model and learning algorithms to predict the state-dependent confidence necessary for calculating the EVAR in time-varying datasets. We discuss applications of the data-driven EVAR to a monitoring problem, in which a DDDAS agent has to chose a set of sensing locations in order to maximize the expected EVAR of the acquired data. In this way, the DDDAS agent can learn a model on an underlying phenomenon of interest by prioritizing the areas where the model is most likely incorrect but highly valued. We empirically demonstrate the efficacy of the presented model and learning algorithms on five real-world datasets. We show that, overall, the EVAR-Real-time Adative Prediction of Time-varying and Obscure Rewards (EVAR-RAPTOR) algorithm outperforms EVAR-Predicted Information Gain* (EVAR-PIG*) as well as naive searches such as random and sequential search across these five real-world datasets.

Organizations today face increasing complexity that demands agile, data-driven decision-making. Geospatial analytics, business intelligence, HR strategy, and AI-enabled database systems have each been studied independently; however, there remains a gap in their integration as a unified organizational intelligence framework. This study developed and implemented a multi-layer framework that integrates spatial data processing, BI dashboards, HR performance indicators, and AI-driven predictive models. Data from healthcare operations, environmental compliance, and enterprise HR systems were cleaned, geocoded, and analyzed. Database optimization strategies, including indexing and query tuning, were applied to improve processing speed. Stakeholder validation was conducted through interviews and scenario simulations to ensure usability and operational relevance. The framework achieved a 40% reduction in query execution time, 25% improvement in preprocessing efficiency, and a 30% faster model inference rate. Healthcare facilities experienced shorter patient bed reallocation cycles, environmental agencies improved inspection coverage by 15%, and enterprises saw a 12% rise in workforce productivity. This integrated approach demonstrates that combining geospatial analytics, BI, HR strategy, and AI creates scalable, context-aware decision-support systems that enhance efficiency, predictive accuracy, and organizational agility across sectors.

In this study, the prediction errors of various spatial interpolation methods used to model values at unmeasured locations was compared and the accuracy of these predictions was evaluated. The root mean square (RMS) was calculated by processing different parameters associated with spatial interpolation by using techniques such as inverse distance weighting, kriging, local polynomial interpolation and radial basis function to known elevation data of the east coastal area under the same condition. As a result, a circular model of simple kriging reached the smallest RMS value. Prediction map using the multiquadric method of a radial basis function was coincident with the spatial distribution obtained by constructing a triangulated irregular network of the study area through the raster mathematics. In addition, better interpolation results can be obtained by setting the optimal power value provided under the selected condition.

Microsoft SQL Server 2008 introduces new geography and geometry spatial datatypes that enable the storage of structured data describing the shape and position of objects in space. This is an interesting and exciting new feature, with many potentially useful applications. Beginning Spatial with SQL Server 2008 covers everything you need to know to begin using these new spatial datatypes, and explains how to apply them in practical situations involving the spatial relationships of people, places, and things on the earth. All of the spatial concepts introduced are explained from the ground up, so you need not have any previous knowledge of working with spatial data. Every section is illustrated with code examples that you can use directly in SQL Server. All of the topics covered in this book apply to all versions of SQL Server 2008, including the freely available SQL Server 2008 Express. What youll learn Understand the fundamental concepts involved in working with spatial data, including spatial references and coordinate systems. Apply these concepts in the collection and storage of spatial data in SQL Server 2008, using the new geometry and geography field types. Create different types of spatial data objectspoints, lines, and polygonsand use these to describe realworld objects. Learn how to analyze spatial data using a range of supported methods, and be aware of a number of different practical applications for these methods. Be shown how to integrate SQL Server with other tools, such as Microsoft Virtual Earth, to display a visual representation of spatial data. Know how to ensure the performance of spatially enabled databases by creating appropriate spatial indexes. Who is this book for? SQL Server developers who wish to use spatial data in Microsoft SQL Server 2008. About the Apress Beginning Series The Beginning series from Apress is the right choice to get the information you need to land that crucial entrylevel job. These books will teach you a standard and important technology from the ground up because they are explicitly designed to take you from novice to professional. Youll start your journey by seeing what you need to knowbut without needless theory and filler. Youll build your skill set by learning how to put together realworld projects step by step. So whether your goal is your next career challenge or a new learning opportunity, the Beginning series from Apress will take you thereit is your trusted guide through unfamiliar territory!

As the amount of data to process increases, a scalable and efficient horizontal database partitioning method becomes more important for OLAP query processing in parallel database platforms. Existing partitioning methods have a few major drawbacks such as a large amount of data redundancy and not supporting join processing without shuffle in many cases despite their large data redundancy. We elucidate the drawbacks arise from their tree-based partitioning schemes and propose a novel graph-based database partitioning method called GPT that improves query performance with lower data redundancy. Through extensive experiments using three benchmarks, we show that GPT significantly outperforms the state-of-the-art method in terms of both storage overhead and query performance.

With the development of Scalable Vector Graphics (SVG) used in Location Based Services (LBS), the presentation and organization of spatial data based SVG became a burning question. The paper extracted the key technologies about presentation and organization of SVG_based spatial data according to the trend of development. The main points concluded acquisition of SVG data, presentation of attributes, construction of spatial data, and compression of SVG data etc. The paper also pointed out some current problems and research orientations in the future in some areas which concluded transform from multiple heterogeneous data to SVG data, presentation and construction of SVG_based data, and data compression etc. The paper can serve as a guide to used SVG in LBS.