Query Time Research Articles

A robust, fast, and accurate algorithm for point in spherical polygon classification with applications in geoscience and remote sensing

The point in spherical polygon problem addresses the task of classifying an arbitrary query point as inside, outside, or on the boundary of a polygon drawn using great circle segments on the spherical surface. Point in spherical polygon has numerous applications in the geosciences and remote-sensing, where the problem of sorting point position data onto geographic regions on the surface of the earth or sensor field-of-view often arises. By resorting to a direct solution on the spherical surface, distortions arising from projection can be avoided. We examine the problem, with and without preprocessing, where preprocessing is a step applied when a large number of classifications are evaluated against the same spherical polygon. Several improvements are introduced to the existing point in spherical polygon algorithm (which does not include preprocessing), resulting in significantly faster inclusion queries and better handling of edge cases. Furthermore, we introduce a preprocessing algorithm by means of recursive, nonuniform spatial subdivision of longitudinal regions (“lunes”) on the sphere. With an O(nlogn) preprocessing step and O(n) space requirement, the algorithm decreases query time from O(n) to O(logn) for most “realistic” polygons found in geoscience and remote sensing applications. Several empirical tests demonstrate that the algorithm performs to theoretical expectations.

Social data provenance framework based on zero-information loss graph database

Social media has become a common platform for global communication across the world due to its rapid dissemination of information among a large audience. Its popularity has raised a crucial challenge to capture the social data provenance of a piece of information published on social media. Social data provenance describes the source and deriving process of a digital content, and when it is updated since its existence? It aids in determining reliability, authenticity, and trustworthiness of a piece of information and explaining how, when, and by whom this information is published. In this paper, we propose a social data provenance (SDP) framework based on zero-information loss graph database (ZILGDB). The proposed framework supports historical data queries, and querying through time using updates management in ZILGDB. It has the capability to capture provenance for a query set including select, aggregate, and data update queries with insert, delete, and update operations. It also provides a detailed provenance analysis through visualization and with efficient multi-depth provenance querying support, to determine both direct and indirect sources of a digital content. We conduct a real-life use case study to evaluate the usefulness of proposed framework in terrorist attack investigation. We evaluate the performance of proposed framework in terms of average execution time for various provenance queries, and provenance capturing overhead for a query set.

Shortest-path queries on complex networks

The shortest-path query, which returns the shortest path between two vertices, is a basic operation on complex networks and has numerous applications. To handle shortest-path queries, one option is to use traversal-based methods (e.g., breadth-first search); another option is to use extension-based methods, i.e., extending existing methods that use indexes to handle shortest-distance queries to support shortest-path queries. These two types of methods make different trade-offs in query time and space cost, but comprehensive studies of their performance on real-world graphs are lacking. Moreover, extension-based methods usually use extra attributes to extend the indexes, resulting in high space costs. To address these issues, we thoroughly compare the two types of methods mentioned above. We also propose a new extension-based approach, Monotonic Landmark Labeling (MLL), to reduce the required space cost while still guaranteeing query time. We compare the performance of different methods on ten large real-world graphs with up to 5.5 billion edges. The experimental results reveal the characteristics of various methods, allowing practitioners to select the appropriate method for a specific application.

Unsupervised Appearance Map Abstraction for Indoor Visual Place Recognition With Mobile Robots

Visual Place Recognition (VPR), the task of identifying the place where an image has been taken from, is at the core of important robotic problems as relocalization, loop-closure detection or topological navigation. Even for indoors, the focus of this work, VPR is challenging for a number of reasons, including real-time performance when dealing with large image databases ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim10^{4}$</tex-math></inline-formula> ) (probably captured by different robots), or the avoidance of Perceptual Aliasing in environments with repetitive structures and scenes. In this letter, we tackle these issues by proposing an off-line mapping technique that abstracts a dense database of georeferenced images without particular order into a Multivariate Gaussian Mixture Model, by creating soft clusters in terms of their similarity in both pose and appearance. This abstract representation is obtained through an Expectation-Maximization algorithm and plays the role of a simplified map. Since querying this map yields a probability of being in a cluster, we exploit this “belief” within a Bayesian filter that regards previous query images and a topological map between clusters to perform more robust VPR. We evaluate our proposal in two different indoor datasets, demonstrating comparable VPR precision to querying the full database while incurring in shorter query times and handling Perceptual Aliasing for sequential navigation.

Fast and Simple Compact Hashing via Bucketing

Compact hash tables store a set S of n key-value pairs, where the keys are from the universe U = {0,ldots ,u-1}, and the values are v-bit integers, in close to {{mathcal {B}}(u, n)} + nv bits of space, where {{mathcal {B}}(u, n)} = log _2 {{u} atopwithdelims (){n}} is the information-theoretic lower bound for representing the set of keys in S, and support operations insert, delete and lookup on S. Compact hash tables have received significant attention in recent years, and approaches dating back to Cleary [IEEE T. Comput, 1984], as well as more recent ones have been implemented and used in a number of applications. However, the wins on space usage of these approaches are outweighed by their slowness relative to conventional hash tables. In this paper, we demonstrate that compact hash tables based upon a simple idea of bucketing practically outperform existing compact hash table implementations in terms of memory usage and construction time, and existing fast hash table implementations in terms of memory usage (and sometimes also in terms of construction time), while having competitive query times. A related notion is that of a compact hash ID map, which stores a set {hat{S}} of n keys from U, and implicitly associates each key in {hat{S}} with a unique value (its ID), chosen by the data structure itself, which is an integer of magnitude O(n), and supports inserts and lookups on {hat{S}}, while using space close to {{mathcal {B}}(u,n)} bits. One of our approaches is suitable for use as a compact hash ID map.

Staggered HLL: Near-continuous-time cardinality estimation with no overhead

Most of existing cardinality estimation algorithms do not support natively interval queries under a sliding window model and are thereby insensitive to data recency. We present Staggered-HyperLogLog (ST-HLL), a probabilistic data structure that takes inspiration from HyperLogLog (HLL) and provides nearly continuous-time estimation of cardinality rates, rather than absolute counts. Our solution has zero-bit overhead with respect to vanilla HLL and negligible additional computational complexity. It is based on a periodic staggered reset of HLL registers and a register equalization operation at query times to compensate for staggered counting. We tested ST-HLL over both synthetic and real Internet traffic traces, showing its ability to track variations of the flow cardinality, quickly adapting to variations under non-stationary flow arrival processes. We show that for the same amount of memory footprint, our algorithm improves the accuracy up to a factor 2x with respect to the state-of-the-art solution, Sliding HLL.

Dynamic Kernels for Hitting Sets and Set Packing

Computing small kernels for the hitting set problem is a well-studied computational problem where we are given a hypergraph with n vertices and m hyperedges, each of size d for some small constant d, and a parameter k. The task is to compute a new hypergraph, called a kernel, whose size is polynomial with respect to the parameter k and which has a size-k hitting set if, and only if, the original hypergraph has one. State-of-the-art algorithms compute kernels of size k^d (which is a polynomial as d is a constant), and they do so in time mcdot 2^d {text {poly}}(d) for a small polynomial {text {poly}}(d) (which is linear in the hypergraph size for d fixed). We generalize this task to the dynamic setting where hyperedges may continuously be added or deleted and one constantly has to keep track of a size-k^d kernel. This paper presents a deterministic solution with worst-case time 3^d {text {poly}}(d) for updating the kernel upon inserts and time 5^d {text {poly}}(d) for updates upon deletions. These bounds nearly match the time 2^d {text {poly}}(d) needed by the best static algorithm per hyperedge. Let us stress that for constant d our algorithm maintains a hitting set kernel with constant, deterministic, worst-case update time that is independent of n, m, and the parameter k. As a consequence, we also get a deterministic dynamic algorithm for keeping track of size-k hitting sets in d-hypergraphs with update times O(1) and query times O(c^k) where c = d - 1 + O(1/d) equals the best base known for the static setting.

A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. To fill this gap, we have conducted a rigorous empirical study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems still need to resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization.

Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

With the rapid development of the technologies of virtual reality (VR) and augmented reality (AR), the accurate visual query of 3D objects which are closer to real world has become a hotspot of study. There are a lot of redundancy problems in the existing axis-aligned bounding box model (AABB) and oriented bounding box model (OBB), which are used to represent 3D objects mostly at present, and cannot be used for the accurate visual query. For example, the results of the visual query must be accurate, and without any error in automatic driving technology in the automotive industry, otherwise, serious safety problems would be caused. The convex hull model could be used to solve those problems. However, there are few studies on visual query based on the convex hull model. A visual query method for 3D obstacle space based on the convex hull model is proposed in this paper. Firstly, the definitions of “query point-object visual body” and “combined obstacle” are proposed. Secondly, the visibility detection algorithm of the convex hull model is given in detail. The collision detection method of “query point-object visible body” combined with other obstacles and line-plane geometric calculation operations are used to detect the visibility of the convex hull model. Then, a 3D visual k-nearest neighbor algorithm based on the convex hull model is given in the paper. Finally, the algorithm is verified by experiments and compared with the traditional visibility detection algorithm, and the analysis of experimental results shows that the algorithm has a great performance and higher accuracy. The growth rate of the query time is smaller, and the speed is faster; especially, when there are fewer query points, the query speed can be increased by more than 50%.

An Efficient Geolocation Method for Malicious LBSD Users Based on Dynamic Adjustment of Probes

The integration of the Internet of Things (IoT) and social networks is a promising trend of network technology. However, the diversity of social networks also poses potential risks to IoT security. Researching on the geolocation of social network users can verify the effectiveness of location protection mechanisms adopted by service providers, as well as provide a means for geolocating miscreants in social networks. Most current research focuses on how to infer the true location of a target within a specific region, such as within a city, while less research has been done on how to achieve fast and accurate localization of targets under long-range conditions. In this manuscript, an efficient localization method for LBSD users at long distances based on dynamic adjustment of probes (DAPL) is proposed. Based on the analysis of factors that affect the accuracy and efficiency of the target location approximation, DAPL can approach the real location sustainability of the target by dynamically generating probe locations. By identifying abnormal fluctuations of the target’s reported distance, timely corrections of probe location are made to improve efficiency. In experimental results for Momo, a global popular LBSD social platform with more than 115 million active users show that even the initial probe is thousands of miles away from the target, DAPL can geolocate the target with a success rate close to 100% (99.5%), which is much higher than 70.6% of the existing method. Only about 12 times of LBSD service queries are needed, and DAPL can geolocate 88.9% of targets within 40 meters with an average error of 22.1 meters, which has higher efficiency and approximate accuracy compared with the existing typical method.

Intrest in influenza and influenza like illnesses in Poland 2016-2021 based on Google Trend's data analysis.

Influenza and Influenza Like Illness (ILI) are viral diseases that show seasonality in incidence. Influenza can be effectively prevented with immunization. Vaccination is recommended for risk groups, especially for the elderly, immunocompromised population, with chronic diseases and pregnant women. The purpose of the study was to assess interest in influenza and influenza vaccine in Poland by users of the Google search engine over the last five years using the Google Trends (GT) tool. Data collected from Google Trends (GT) over a five-year period beginning on 11/9/2016 was analyzed. The time trend analysis focused at four phrases in Polish "grypa", "grypa objawy", "szczepionka przeciw grypie" and "szczepionka na grypę", which can be translated into English, as; "flu", "flu symptoms", "flu vaccine", and a colloquial phrase meaning "against the flu vaccine". Before the outbreak of the COVID-19 pandemic, a seasonal increase in inquiries is evident, mimicking the increase in influenza and ILI cases or an increase in interest in the topic during the season in which prophylaxis is carried out using available vaccines. During the COVID-19 pandemic, the seasonal interest in the above topic was disrupted. During the COVID-19 pandemic, there were changes in the search terms in Polish. This included the timing and amount of queries related to influenza-themed expressions. During the COVID-19 pandemic, there was an increase in influenza and ILI queries during the period of increased incidence of COVID-19.

Improving Time-Dependent Contraction Hierarchies

Computing time-optimal shortest paths, in road networks, is one of the most popular applications of Artificial Intelligence. This problem is tricky to solve because road congestion affects travel times. The state-of-the-art in this area is an algorithm called Time-dependent Contraction Hierarchies (TCH). Although fast and optimal, TCH still suffers from two main drawbacks: (1) the usual query process uses bi-directional Dijkstra search to find the shortest path, which can be time-consuming; and (2) the TCH is constructed w.r.t. the entire time domain T, which complicates the search process for queries q that start and finish in a smaller time period Tq ⊂ T. In this work, we improve TCH by making use of time-independent heuristics, which speed up optimal search, and by computing TCHs for different subsets of the time domain, which further reduces the size of the search space. We give a full description of these methods and discuss their optimality-preserving characteristics. We report significant query time improvements against a baseline implementation of TCH.

Can the United States' Google Search Algorithm be Reflective of Implicit Racial Bias?

Given the sociological consensus of systemic and implicit racial bias in many American institutions, the interactions of those systems and their sources with computer science and algorithms, specifically Google Search, are largely unknown. The current literature aiming to research the relationship between implicit bias and computer algorithms, such as “Algorithms of Oppression: How Search Engines Reinforce Racism” by Dr. Safiya Noble and “Race After Technology: Abolitionist Tools for the New Jim Code” by Professor Ruha Benjamin, seems to demonstrate that there is evidence or precedence of computer algorithms having implicit racial bias. Given the current state of the literature on this topic, this study aims to demonstrate whether the United States’ Google Search algorithm is reflective of implicit racial bias.&#x0D; &#x0D; With the hypothesis that the United States’ Google Search algorithm is reflective of implicit racial bias, in order to test this hypothesis, the method would require the collection of data, mean search times, the organization of data into normal distributions, and the use of statistical analysis, hypothesis testing, to determine an algorithmic bias. The results suggest that with specific occupational search queries, Google’s search algorithm seems to have faster mean search times for white occupational search queries than racial minority occupational search queries and tend to be significantly different from occupational search queries and White occupational search queries. Given the evidence performed by hypothesis testing, this research paper assertively concludes that the United States’ Google search algorithm is reflective of implicit racial bias.

String indexing for top-k close consecutive occurrences

The classic string indexing problem is to preprocess a string S into a compact data structure that supports efficient subsequent pattern matching queries, that is, given a pattern string P, report all occurrences of P within S. In this paper, we study a basic and natural extension of string indexing called the string indexing for top-k close consecutive occurrences problem (Sitcco). Here, a consecutive occurrence is a pair (i,j), i<j, such that P occurs at positions i and j in S and there is no occurrence of P between i and j, and their distance is defined as j−i. Given a pattern P and a parameter k, the goal is to report the top-k consecutive occurrences of P in S of minimal distance. The challenge is to compactly represent S while supporting queries in time close to the length of P and k. We give three time-space trade-offs for the problem. Let n be the length of S, m the length of P, and ϵ∈(0,1]. Our first result achieves O(nlog⁡n) space and optimal query time of O(m+k). Our second and third results achieve linear space and query times either O(m+k1+ϵ) or O(m+klog1+ϵ⁡n). Along the way, we develop several techniques of independent interest, including a new translation of the problem into a line segment intersection problem and a new recursive clustering technique for trees.

VIP hashing

All data is not equally popular. Often, some portion of data is more frequently accessed than the rest, which causes a skew in popularity of the data items. Adapting to this skew can improve performance, and this topic has been studied extensively in the past for disk-based settings. In this work, we consider an in-memory data structure, namely hash table , and show how one can leverage the skew in popularity for higher performance. Hashing is a low-latency operation, sensitive to the effects of caching and code complexity, among other factors. These factors make learning in-the-loop challenging as the overhead of performing additional operations can have significant impact on performance. In this paper, we propose VIP hashing, a hash table method that uses lightweight mechanisms for learning the skew in popularity and adapting the hash table layout on the fly. These mechanisms are non-blocking, i.e, the hash table is operational at all times. The overhead is controlled by sensing changes in the popularity distribution to dynamically switch-on/off the mechanisms as needed. We ran extensive tests against a host of workloads generated by Wiscer , a homegrown benchmarking tool, and we find that VIP hashing improves performance in the presence of skew (22% increase in fetch operation throughput for a hash table with 1M keys under low skew) while adapting to insert and delete operations, and changing popularity distribution of keys on the fly. Our experiments on DuckDB show that VIP hashing reduces the end-to-end execution time of TPC-H query 9 by 20% under low skew.

Special Section on the Fifty-Second Annual ACM Symposium on the Theory of Computing (STOC 2020)

This issue of SICOMP contains six specially selected papers from STOC 2020, the Fifty-second Annual ACM Symposium on the Theory of Computing, which was held June 22--26, 2020, initially planned at Chicago, Illinois, but due to COVID-19 was an online conference in the end. The papers here were chosen to represent the range and quality of the STOC program. These papers have been revised and extended by their authors and subjected to the standard thorough reviewing process of SICOMP. The program committee for STOC 2020 consisted of an executive committee made up of Nima Anari, Boaz Barak, Sébastien Bubeck, Mark Bun, Arkadev Chattopadhyay, Chandra Chekuri, Julia Chuzhoy, Marek Cygan, Ilias Diakonikolas, Yevgeniy Dodis, Sebastian Forster, Ankit Garg, Nika Haghtalab, Prahladh Harsha, Justin Holmgren, Piotr Indyk, Rahul Jain, Sanjeev Khanna, Dakshita Khurana, Pravesh Kothari, Robert Krauthgamer, Marvin Künnemann, Tengyu Ma, Rafael Oliveira, Merav Parter, Sofya Raskhodnikova, Robert Robere, Dana Ron, Noga Ron-Zewi, Thatchaphol Saranurak, Balasubramanian Sivan, Christian Sohler, Madhur Tulsiani, Omri Weinstein, Christian Wulff-Nilsen, and Henry Yuen. The program chair was Julia Chuzhoy. Included in this issue are the following papers: ``Explicit Near-Ramanujan Graphs of Every Degree" by Sidhanth Mohanty, Ryan O'Donnell, and Pedro Paredes shows a deterministic poly$(n)$-time algorithm that outputs a $d$-regular graph on $\Theta(n)$ vertices that is $\epsilon$-near-Ramanujan. ``Reducing Path TSP to TSP" by Vera Traub, Jens Vygen, and Rico Zenklusen presents a black-box reduction from the path version of the traveling salesman problem (Path TSP) to the classical tour version (TSP). ``Nearly Optimal Static Las Vegas Succinct Dictionary" by Huacheng Yu obtains a randomized dictionary data structure using ${OPT}+{poly}\lg n+O(\lg^{(\ell)} U)$ bits of space with expected constant query time for the static dictionary problem. ``Separating the Communication Complexity of Truthful and Nontruthful Algorithms for Combinatorial Auctions" by Sepehr Assadi, Hrishikesh Khandeparkar, Raghuvansh Raj Saxena, and S. Matthew Weinberg provides the first separation in the approximation guarantee achievable by truthful and nontruthful combinatorial auctions with polynomial communication. ``Strong Average-Case Circuit Lower Bounds from Nontrivial Derandomization" by Lijie Chen and Hanlin Ren establish a connection between nondeterministic algorithms estimating the acceptance probability of a given circuit and average-case lower bounds for nondeterministic time classes. ``Improved Bounds for Perfect Sampling of $k$-Colorings in Graphs" by Siddharth Bhandari and Sayantan Chakraborty presents a randomized algorithm that takes as input an undirected $n$-vertex graph $G$ with maximum degree $\Delta$ and an integer $k>3\Delta$ and returns a random proper $k$-coloring of $G$. We thank the authors, the STOC 2020 program committee, the STOC 2020 external reviewers, and the SICOMP referees for all of their hard work. Arkadev Chattopadhyay, Marek Cygan, Noga Ron-Zewi, Christian Wulff-Nilsen - Guest editors

Nearly Optimal Planar $k$ Nearest Neighbors Queries under General Distance Functions

We study the $k$ nearest neighbors problem in the plane for general, convex, pairwise disjoint sites of constant description complexity such as line segments, disks, and quadrilaterals under a general family of distance functions including the $L_p$ norms and additively weighted Euclidean distances. We compose a static data structure for this general setting with nearly optimal $O(n\log\log n)$ space, optimal $O(\log n+k)$ query time, and optimal expected $O(n\log n)$ preprocessing time. We also devise a dynamic data structure (that allows insertions and deletions of sites) with $O(n\log n)$ space, $O(\log^2n+k\log n)$ query time, and expected amortized $O(\log^2 n)$ insertion time and $O(\log^4n)$ deletion time, matching the best known time complexities for point sites in the Euclidean metric and improving many applications such as dynamic minimum spanning trees in a general planar metric and dynamic connectivity in disk intersection graphs. Our results, to some extent, indicate that for the $k$ nearest neighbors problem, general distance functions may share the same time complexities with point sites in the Euclidean metric. To achieve this progress, we design vertical shallow cuttings of linear size for general distance functions. Vertical shallow cuttings are a key technique to tackle the $k$ nearest neighbors problem for point sites in the Euclidean metric, while existing generalizations to general distance functions are either not vertical or not of linear size. Our innovation is a new random sampling technique for the analysis of geometric structures, and since this technique provides a new way to develop geometric algorithms, we believe it is of independent interest.

X-FIST: Extended flood index for efficient similarity search in massive trajectory dataset

Similarity search tasks in big trajectory datasets often require tree-based indices to shorten the query time through early pruning of dissimilar trajectories early. However, tree-based indices have been outperformed by the learned index in skewed-distribution datasets of multidimensional point experimentally. The learned index performed faster because of its data distribution awareness and machine learning model-based prediction. Directly applying learned index to trajectories can lead to inefficient query performance due to repeating range queries according to the query trajectory length. Thus, we develop X-FIST, an extended Flood index to learn the Minimum Bounding Region of the trajectories and their sub-trajectories. In similarity search, X-FIST prunes dissimilar trajectories effectively independent to the query trajectory length. If the trajectory similarity distance function changes, X-FIST does not need to train new models of its Flood index. The experimental results on three real-world trajectory datasets demonstrate that our approach shortened query time in every distance function and produced better storage size reduction than the tree-based index and direct approach of learned index.

One step at a time: An efficient approach to query-based ontology debugging

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for a semi-automatic fault localization in ontologies have been proposed and extensively studied. One class of these approaches involve a human expert who provides answers to system-generated queries about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting expert.In this work, we demonstrate that these assumptions might not always be adequate and discuss consequences of their violations. In particular, we characterize a range of expert types with different query answering behavior and show that existing approaches are far from achieving optimal efficiency for all of them. In addition, we find that the cost metric adopted by state-of-the-art techniques might not always be realistic and that a change of metric has a decisive impact on the best choice of query answering strategy. As a remedy, we suggest a new – and simpler – type of expert question that leads to a stable fault localization performance for all analyzed expert types and effort metrics, and has numerous further advantages over existing techniques. Moreover, we present an algorithm which computes and optimizes this new query type in worst-case polynomial time and which is fully compatible with existing concepts (e.g., query selection heuristics) and infrastructure (e.g., debugging user interfaces) in the field.Comprehensive experiments on faulty real-world ontologies attest that the new querying method is substantially and statistically significantly superior to existing techniques both in terms of the number of necessary expert interactions and in terms of the query computation time. We find that relying on the new querying method can save an interacting expert more than 80% of their work, and can reduce the expert’s waiting time for the next query by more than three orders of magnitude. Beside these findings, we demonstrate that the efficiency of existing query-based tools can be significantly boosted by suggesting an appropriate query answering strategy to an expert; we also make recommendations in this regard. Further, we suggest optimal configurations of a debugger for situations where the new type of query is used.Remarkably, the proposed approach is not only applicable to ontologies, but to any monotonic knowledge representation language, and can even be adopted to solve general model-based diagnosis problems expressible using Reiter’s theory.

A Study on Resource Security under Legal Protection of e-Commerce Data Sovereignty Based on Blockchain Network

With the growing demand for cross-border electronic data forensics, national sovereignty and international network security have ushered in great challenges, and how to achieve dynamic balance in various fields under cross-border electronic data forensics, advance the process of global cyberspace governance, reach international consensus, and achieve win-win cooperation is a question that China needs to solve. Based on the above status quo, a blockchain-based secure e-commerce system is researched and implemented. Using the distributed architecture of the blockchain system and the automatic execution feature of smart contracts, a secure transaction application system is designed to realize decentralization and solve the problem of untrustworthy intermediate parties. Data storage is based on the mechanism of combining blockchain and offchain databases to ensure data security, and the characteristic of blockchain can only be appended and read to prevent the system from being tampered. The database index and the storage structure of smart contract commodity information and transaction information are established to improve the query efficiency. The functional and performance tests of the system show that the transaction processing efficiency is high in the multinode environment, and the response time of order and commodity status query can reach 80 ms. The design and development process of the system is introduced in detail, which is an important reference value for the application of blockchain technology in e-commerce.