The rapid advancement of generative models has significantly influenced various domains, including Remote Sensing (RS), through applications in map generation, navigation, and environmental monitoring. However, alongside these positive impacts, the misuse of generative models has raised critical concerns, such as adversarial attacks. Existing forensic detection studies rely on publicly available datasets but often lack a focus on map images, which are essential for navigation and critical decision-making tasks. To bridge this gap, we introduce TerraFly-Forensics, a comprehensive dataset comprising real and synthesized map images generated using advanced generative models. The real maps were downloaded using the TerraFly mapping system, and state-of-the-art generative models were applied to produce high-quality synthesized map images, accompanied by a detailed evaluation of their generative quality. We have conducted an experimental evaluation of several recent forensic detection methods on the TerraFly-Forensics dataset, providing insights into and contributing a new foundation for forensic detection in Remote Sensing.

Dynamic urban development and natural disasters often lead to discrepancies between maps and satellite images, posing challenges for autonomous navigation and smart city planning. Automatic map generation from satellite imagery is crucial for addressing these discrepancies and ensuring accurate geospatial data. While Generative Adversarial Networks (GANs) favored with contrastive learning have been used for this task, limitations such as randomness in feature selection result in suboptimal performance, and MLP-based projection heads lack the expressiveness needed for effective feature generation. To address these limitations, we propose a novel model, KAN-Attn, which incorporates an attention-based query selection mechanism for selecting relevant features in contrastive learning. Also, KAN-Attn is the first application of Kolmogorov-Arnold Network (KAN) to improve feature generation with enhanced expressiveness in map generation. Our innovations allow KAN-Attn to achieve optimal performance in map generation, with experiments on publicly available datasets demonstrating state-of-the-art results based on relevant metrics on perceptual quality.

Non-stationarity is often observed in Geographic datasets. One way to explain non-stationarity is to think of it as a hidden "local knowledge" that varies across space. It is inherently difficult to model such data as models built for one region do not necessarily fit another area as the local knowledge could be different. A solution for this problem is to construct multiple local models at various locations, with each local model accounting for a sub-region within which the data remains relatively stationary. However, this approach is sensitive to the size of data, as the local models are only trained from a subset of observations from a particular region. In this paper, we present a novel approach that addresses this problem by aggregating spatially similar sub-regions into relatively large partitions. Our insight is that although local knowledge shifts over space, it is possible for multiple regions to share the same local knowledge. Data from these regions can be aggregated to train a more accurate model. Experiments show that this method can handle non-stationary and outperforms when the dataset is relatively small.

This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.

This study explored interactions between users and a 3D virtual world using a multi-touch display and sought to identify the advantages of this form of interaction. An experiment was conducted with 28 subjects, who were classified into two groups: experienced gamers and casual gamers. The experiment involved a pseudo-universe, where users were required to find five objects using the multi-touch display, and five objects using a game controller (GamePad). Overall, multi-touch was slightly faster than the GamePad in the search for objects. Experienced users performed significantly better when using the GamePad, compared to casual gamers. When casual gamers were analyzed separately, their performance was better (at a statistically significant level) with the multi-touch display, compared to the GamePad. Finally, users had a significantly higher error rate when switching from the GamePad to the keyboard, than when switching from the multi-touch to the keyboard.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction technique. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We looked at 23 participants and 20 distinct referents (with multiple gestures per referent). We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures. In some instances, gestures were derived from previous interactions but were still appropriate for the environment presented. This study provides a rich set of information and useful recommendations for future designers and/or developers.

CubeVR incorporates the traditional studio-based learning in architecture with virtual reality (VR) and in doing so it enables features that are not available or difficult to achieve in the real-world. CubeVR allows students and instructors to move from the existing studio to a virtual environment, allowing for a more impactful learning experience. This paper presents our current state of the CubeVR prototype, informal impressions of users, and the use of different affordances in the virtual world.

Understanding gaming expertise is important in user studies. We present a study comprised of 60 participants playing a First Person Shooter Game (Counter-Strike: Global Offensive). This study provides results related to a keyboard model used to determine an objective measurement of gamers' skill. We also show that there is no correlation between frequency questionnaires and user skill.

Clinical research and drug development trials generate large amounts of data. Due to the dispersed nature of clinical trial data across multiple sites and heterogeneous databases, it remains a challenge to harness these trial data for analytics to gain more understanding about the implementation of studies as well as disease processes. Moreover, the veracity of the results from analytics is difficult to establish in such datasets. We make a two-fold contribution in this paper: First, we provide a mechanism to extract task-relevant data using Master Data Management (MDM) from a clinical trial database with data spread over several domain datasets. Second, we provide a method for validating findings by collaborative utilization of multiple data mining techniques, namely: classification, clustering, and association rule mining. Overall, our approach aims at extracting useful knowledge from data collected during clinical trials to enable the development of faster and cheaper clinical trials that more accurate and impactful. For a demonstration of the efficacy of our proposed methods, we utilized the following datasets: (1) the National Institute on Drug Abuse (NIDA) data share repository and (2) the data from the Osteoarthritis initiative (OAI), where we found real-world implications in validating the findings using multiple data mining methods in a collaborative manner. The comparative results with existing state of the art techniques show the usefulness and high accuracy of our methods.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

Timetable regularity, that is, equability of headways, is an important measure for service quality in high frequency public transit systems, assuring an evenly distributed passenger load as well as improving product attractiveness. However, to be feasible during daily operation a timetable may also have to adhere to other planning requirements, such as departure time coordination with other service providers or deliberately short headways to reduce the passenger load of follow‐up vehicles. In this article, a disjunctive program formulation combining aspects of two previous optimization models is proposed, to generate regular public transit timetables adhering to planning requirements. The modeled requirements not only allow for the consideration of feasibility constraints from daily operations, but also for the consideration of simultaneous departures for transfer connections, an objective traditionally opposed to regularity. To show its applicability the approach is applied to two models of artificial transit networks as well as to models of the public transit network of Cologne, Germany. The results show that the proposed formulation can be used to generate timetables for network instances of realistic size in acceptable time. For networks consisting of multiple connected components it is shown that a decomposition approach can significantly reduce run times.

Ride sourcing services such as Uber and Lyft have become widespread in large cities for everyday mobility. When matching passengers, these services attempt to optimize cost savings at a global level. However, a possible scenario is that a passenger A is matched to passenger B even though if A were matched to passenger C, then both A and C would have saved more money. This introduces the concept of "fairness" in ride sharing, which consists of finding the Nash equilibrium in ridesharing. In this paper we compare optimum and fair ridesharing theoretically and experimentally. We show that although theoretically the gap between fair and optimum is large, in practice it is very small.

CircGR is a multi-touch non-symbolic gesture recognition algorithm, which utilizes circular statistic measures to implement linearithmic (O(n log n)) template-based matching. CircGR provides a solution to gesture designers, which allows for building complex multi-touch gestures with high-confidence accuracy. We demonstrated the algorithm and described a user study with 60 subjects and over 12,000 gestures collected for an original gesture set of 36. The accuracy is over 99% with the Matthews correlation coefficient of 0.95. In addition, early gesture detection was successful in CircGR as well.

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness.

A complex fuzzy class is characterized by a pure complex fuzzy grade of membership. Pure complex fuzzy classes are paramount in providing rich semantics for cases where the fuzzy data is periodic with a fuzzy period. Often, however, the available data is contaminated by noise, opposing expert opinions, ambiguity, and false information. This opens the door for using intuitionistic fuzzy sets theory: representing the false information via a degree of non-membership. Several researchers have identified the benefits of integrating the two concepts of complex fuzzy sets and intuitionistic fuzzy sets. Nevertheless, complex fuzzy sets allow for only one component of the degree of membership to be fuzzy. In this paper, we introduce the concept of complex intuitionistic fuzzy classes, which are characterized by pure complex intuitionistic fuzzy grade of membership. We define the basic terms and operations on complex intuitionistic fuzzy classes and provide a motivating example of relevant application.

With the exponential growth of the usage of web map services, geo-data analysis has become more and more popular. This article develops an online spatial data analysis and visualization system, TerraFly GeoCloud, which helps end-users visualize and analyze spatial data and share the analysis results. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements. The system is available at http://terrafly.fiu.edu/GeoCloud/.

Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). PROFILR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows that PROFILR is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.

Population density and natural and man-made disasters make public safety a concern of growing importance. In this paper we aim to enable the vision of smart and safe cities by exploiting mobile and social networking technologies to securely and privately extract, model and embed real-time public safety information into quotidian user experiences. We first propose novel approaches to define location- and user-based safety metrics. We evaluate the ability of existing forecasting techniques to predict future safety values. We introduce iSafe, a privacy-preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. We present implementation details of iSafe as both an Android application and a browser plugin that visualizes safety levels of visited locations and browsed geosocial venues. We evaluate iSafe using crime and census data from the Miami-Dade (FL) county as well as data we collected from Yelp, a popular geosocial network.

With the exponential growth of the usage of web map services, the geo data analysis has become more and more popular. This paper develops an online Spatial Data Analysis System, TerraFly GeoCloud, which facilitates the end user to visualize and analyze spatial data, and to share the analysis results. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon TerraFly map supporting many different visualization functions and spatial data analysis models. TerraFly GeoCloud also enables the MapQL technology to create maps using SQL-like statements. The TerraFly GeoCloud system is available at http://terrafly.fiu.edu/GeoCloud/.

GIS application hosts are becoming more and more complicated. Thus, their management is more time consuming, and reliability decreases with the complexity of GIS applications increasing. We have designed, implemented, and evaluated, a virtualized whole Large Scale Distributed Spatial Data Visualization System for optimizing maintainability and performance when handling large amount of GIS data. We employ the virtual machines (VMs) technique, load balance cluster techniques, and autonomic resource management to improve the system's performance. The proposed system was prototyped on TerraFly [1], a production web map service, and evaluated using actual TerraFly workloads. The results show that the virtual TerraFly system has both good performance and much better maintainability. Our experiments show that the proposed Virtual TerraFly Geo-database system has doubled the reliability, and saved 20-30% computing resources cost compared to current static peak-load physical machine node allocations.

With the fast growing use of web-based map services, the performance of indexing and querying of location-based data is becoming a critical quality of service aspect. Spatial indexing is typically time-consuming and is not available to end-users. To address this challenge, we have developed and open-sourced an Online Indexing and Querying System for Big Geospatial Data, sksOpen. Integrated with the TerraFly Geospatial database [1], TerraFly sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user's data analysis.

GIS systems and online services are growing at a very fast pace, however, there are few online services for the analysis of geospatial epidemiology and their functionality is limited. We present a geospatial epidemiology analysis system on the TerraFly Geo-spatial Cloud platform. The system provides comprehensive spatial analysis methods and visualization. In this system, the user is not required to program in order to employ the functionality. All the datasets are stored in the Geo-spatial Cloud. This system is accessible at http://terrafly.fiu.edu/GeoCloud/. The system API algorithms adapted to geospatial epidemiology. The application utilizes the GeoCloud distributed storage system for the Big Data to be analyzed, it utilizes an interactive mapping API to display results.

Storing user friend lists, preferences and messages, online social networks have become a significant source of sensi-tive personal information. A recent addition to this space, geosocial networks (GSNs) such as Yelp [1] or Foursquare [2],

A new thermal imaging framework with unique feature extraction and similarity measurements for face recognition is presented. The research premise is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The proposed algorithm is fully integrated and consolidates the critical steps of feature extraction through the use of morphological operators, registration using the Linear Image Registration Tool and matching through unique similarity measures designed for this task. The novel approach at developing a thermal signature template using four images taken at various instants of time ensured that unforeseen changes in the vasculature over time did not affect the biometric matching process as the authentication process relied only on consistent thermal features. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed an average accuracy of 88.46% for skeletonized signatures and 90.39% for anisotropically diffused signatures. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal imaging based systems. Empirical results applying this approach to an existing database of thermal images proves this assertion.

Providing input to targeted advertising, profiling social network users is an important source of revenue for geosocial networks. Since profiles contain personal information, their construction introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce ProfilR, a suite of mechanisms that construct LCPs in a private and correct manner. Our Android implementation shows that ProfilR is efficient: the end-to-end overhead is small even under strong correctness assurances.

Smart cities combine technology and human resources to improve the quality of life and reduce expenditures. Ensuring the safety of city residents remains one of the open problems, as standard budgetary investments fail to decrease crime levels. This work takes steps toward implementing smart, safe cities, by combining the use of personal mobile devices and social networks to make users aware of the safety of their surroundings. We propose novel metrics to define location and user based safety values. We evaluate the ability of forecasting techniques including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) to predict future safety values. We devise iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile device users and integrate our approach into an Android application for visualizing safety levels. We further investigate relationships between location dependent social network activity and crime levels. We evaluate our contributions using data we collected from Yelp as well as crime and census data.

A spatial similarity join of two geospatial datasets finds pairs of records that are simultaneously similar on spatial and textual attributes. Such join is useful for a variety of applications, like data cleansing, record linkage, duplications detection and geocoding enhancement. Efficient techniques exist for the individual joins on either spatial or textual attributes. However, the combined problem has received much less research attention. This paper presents the SpSJoin (Spatial Similarity join) system to fill in this need. SpSJoin is a platform that merges geospatial and text processing techniques for efficiently performing spatial similarity joins. The platform leverages parallel computing with MapReduce to tackle scalability issues in joining large datasets. The efficiency of the proposed techniques are experimentally validated with a join case for improving the geolocation of entities in a real geospatial dataset with referential entities of another dataset.

Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.

We present a comprehensive Internet data extraction tool adopted for the TerraFly Geographic Information System (GIS). TerraFly is a web-enabled system that allows users to virtually fly over remotely sensed data, including satellite imagery and aerial photography, using a standard Internet browser. The data extraction tool presented here is designed to augment the user's virtual flight experience with extensive data relevant to any given geographical point along the virtual flight path. The data presented to the user is retrieved from several server-side databases and is collected from the Internet data providers using our patented data extraction technology. Some data elements are presented to the user as overlays, some in popup windows, and some via hyper-linking to third-party web sites

Object Relationship Notation (ORN) is a declarative scheme that allows a variety of common relationship types to be conveniently specified to a database management system (DBMS), thereby allowing their semantics to be automatically enforced by the DBMS. ORN can be integrated into any data model that represents binary associations or DBMS that implements them. We give a brief description of ORN syntax and semantics and provide algorithms that can be used to implement ORN. These algorithms must deal with the presence of association cycles in the database. We explore in detail the problems caused by such cycles and how ORN and its implementation deal with them, and we show that ORN semantics are noncircular and unambiguous.

Searching desired data on the Internet is one of the most common ways the Internet is used. No single search engine is capable of searching all data on the Internet. The approach that provides an interface for invoking multiple search engines for each user query has the potential to satisfy more users. When the number of search engines under the interface is large, invoking all search engines for each query is often not cost effective because it creates unnecessary network traffic by sending the query to a large number of useless search engines and searching these useless search engines wastes local resources. The problem can be overcome if the usefulness of every search engine with respect to each query can be predicted. We present a statistical method to estimate the usefulness of a search engine for any given query. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that our estimation method is much more accurate than existing methods.

This paper presents a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, the contents of databases are indicated approximately by database representatives. Databases are ranked using their representatives with respect to the given query. We provide a necessary and sufficient condition to rank the databases optimally. In order to satisfy this condition, we provide three estimation methods. One estimation method is intended for short queries; the other two are for all queries. Second, we provide an algorithm, OptDocRetrv, to retrieve documents from the databases according to their rank and in a particular way. We show that if the databases containing the n most similar documents for a given query are ranked ahead of other databases, our methodology will guarantee the retrieval of the n most similar documents for the query. When the number of databases is large, we propose to organize database representatives into a hierarchy and employ a best-search algorithm to search the hierarchy. It is shown that the effectiveness of the best-search algorithm is the same as that of evaluating the user query against all database representatives.

In a fuzzy relational database where a relation is a fuzzy set of tuples and ill-known data are represented by possibility distributions, nested fuzzy queries can be expressed in the Fuzzy SQL language. Although it provides a very convenient way for users to express complex queries, a nested fuzzy query may be very inefficient to process with the naive evaluation method based on its semantics. In conventional databases, nested queries are unnested to improve the efficiency of their evaluation. In this paper, we extend the unnesting techniques to process several types of nested fuzzy queries. An extended merge-join is used to evaluate the unnested fuzzy queries. As shown by both theoretical analysis and experimental results, the unnesting techniques with the extended merge-join significantly improve the performance of evaluating nested fuzzy queries.

Two typical situations in which it is of practical interest to determine the similarities of text documents to a query due to a search engine are: (1) a global search engine, constructed on top of a group of local search engines, wishes to retrieve the set of local documents globally most similar to a given query; and (2) an organization wants to compare the retrieval performance of search engines. The dot-product function is a widely used similarity function. For a search engine using such a function, we can determine its similarity computations if how the search engine sets the weights of terms is known, which is usually not the case. In this paper, techniques are presented to discover certain mathematical expressions of these formulas and the values of embedded constants when the dot-product similarity function is used. Preliminary results from experiments on the WebCrawler search engine are given to illustrate our techniques. 1 1 Introduction Documents of interest are often found...

Summary form only given. We present optimizations that improve the compression and computational efficiency of Lempel-Ziv (LZ77) and Huffman algorithms. The compression performance of the LZ77 algorithm can be improved by using an optimized longest match parsing strategy. Another factor that can be considered is the size of the reference to the matching string, which can vary due to the secondary Huffman compression. We present an efficient parsing algorithm that considers both factors while minimizing the computational requirements. Our second optimization technique optimizes static Huffman encoding by efficiently dividing the input into blocks of varying size with uneven character frequency distributions.

Metasearch engines can be used to facilitate ordinary users for retrieving information from multiple local sources (text databases). In a metasearch engine, the contents of each local database is represented by a representative. Each user query is evaluated against the set of representatives of all databases in order to determine the appropriate databases to search. When the number of databases is very large, say in the order of tens of thousands or more, then a traditional metasearch engine may become inefficient as each query needs to be evaluated against too many database representatives. Furthermore, the storage requirement on the site containing the metasearch engine can be very large. In this paper, we propose to use a hierarchy of database representatives to improve the efficiency. We provide an algorithm to search the hierarchy. We show that the retrieval effectiveness of our algorithm is the same as that of evaluating the user query against all database representatives. We also show that our algorithm is efficient. In addition, we propose an alternative way of allocating representatives to sites so that the storage burden on the site containing the metasearch engine is much reduced.

In this paper, we present a statistical method to estimate the usefulness of a search engine for any given query. The estimates can be used by a metasearch engine to choose local search engines to invoke. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that the proposed estimation method is quite accurate.

No abstract available.

Traditional sampling-based estimators infer the actual selectivity of a query based purely on runtime information gathering, excluding the previously collected information, which underutilizes the information available. Table-based and parametric estimators extrapolate the actual selectivity of a query based only on the previously collected information, ignoring online information, which results in inaccurate estimation in a frequently updated environment. We propose a novel hybrid estimator that utilizes and optimally combines the online and previously collected information. A theoretical analysis demonstrates that the online and previously collected information is complementary, and that the comprehensive utilization of the online and previously collected information is of value for further performance improvement. Our theoretical results are validated by a comprehensive experimental study using a practical database, in the presence of insert, delete and update operations. The hybrid approach is very promising in the sense that it provides an adaptive mechanism that allows the optimal combination of information obtained from different sources in order to achieve a higher estimation accuracy and reliability.

The object-oriented programming language Java is an ideal companion to an object-oriented database system. This paper describes our approach to provide a seamless application programmer interface. It is based on a modular architecture with components for database engines, a communications protocol and a JAVA API facilitator. The open architecture is flexible, scalable and distributed in nature.

The performance of a mutual exclusion algorithm is measured by the number of messages exchanged per critical section execution and the delay between successive executions of the critical section. There is a message complexity and synchronization delay trade-off in mutual exclusion algorithms. Lamport's (1978) algorithm and Ricart and Agrawal's (1981) algorithm both have a synchronization delay of T, but their message complexity is O(N). Maekawa's (1985) algorithm reduces message complexity to O(/spl radic/N); however, it increases the synchronization delay to 2T. After Maekawa's algorithm, many quorum-based mutual exclusion algorithms have been proposed to reduce message complexity or increase the resiliency to site and communication link failures. Since these algorithms are Maekawa-type algorithms, they also suffer from long synchronization delay 2T. We propose a delay-optimal quorum-based mutual exclusion algorithm which reduces the synchronization delay to T and still has the low message complexity O(K) (K is the size of the quorum, which can be as low as log N). A correctness proof and detailed performance analysis are provided.

A.P. Sistla et al. (1997) designed a similarity-based video retrieval system. Queries were specified in a language called the Hierarchical Temporal Language (HTL). In this paper, we present several extensions of HTL. These extensions include queries that can have the negation operator and any other logical and temporal operators such as disjunction. Efficient algorithms for processing queries in the extended language are also presented.

When a multidatabase system contains textual database systems (i.e., information retrieval systems), queries against the global schema of the multidatabase system may contain a new type of joins-joins between attributes of textual type. Three algorithms for processing such a type of joins are presented and their I/O costs are analyzed in this paper. Since such a type of joins often involves document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ on whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and the simulation results indicate that the relative performance of these algorithms depends on the input document collections, system characteristics, and the input query. For each algorithm, the type of input document collections with which the algorithm is likely to perform well is identified. An integrated algorithm that automatically selects the best algorithm to use is also proposed.

In this paper a non-traditional computer technology for data representation and processing is presented. It is developed on the basis of classic number theory and some results of fundamental research of Russian academicians Chebyshev and Vinogradov. A peculiarity of the algebraic constructions discussed is determined by using special polynomial conversions and mixed number systems. Some moments concerning this specific had been described previously. Special methods of representing the polynomials allow to obtain the product of two complex numbers in parallel mode using only two real multiplications instead of four as well as to obtain the product of two polynomials of a degree 'n' using n multiplications instead of n. This technology is based on applying both a polynomial ring mapping (PRM) and an extended Galois Fields techniques which allows large dynamic range computations to be performed using massively parallel small finite ring computations. Such computations can offer distinct advantages over computations using usual binary number system.

Image retrievaf based on semantic contents involves extraction, modelling and indexing of content information.While extraction of abstract contents is a hard problem, it is only part of the bigger picture.In this paper we use knowledge about the semantic contents of images to improve retrieval effectiveness.In particular we use Word Net, an electronic Iexicaf system for query and database expansion.Our content model facilitates novel uses of WordNet.We also propose a new normalization formula, an object significance scheme and evaluate their effectiveness with real user experiments.We describe the experiment setup and provide quantitative evaluation of each technique.

Article Free Access Share on Performance evaluation of G-tree and its application in fuzzy databases Authors: Chengwen Liu DePaul University, Chicago, Illinois DePaul University, Chicago, IllinoisView Profile , Aris Ouksel University of Illinois at Chicago University of Illinois at ChicagoView Profile , Prasad Sistla University of Illinois at Chicago University of Illinois at ChicagoView Profile , Jing Wu University of Illinois at Chicago University of Illinois at ChicagoView Profile , Clement Yu University of Illinois at Chicago University of Illinois at ChicagoView Profile , Naphtali Rishe Florida International University Florida International UniversityView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 235–242https://doi.org/10.1145/238355.238503Published:12 November 1996Publication History 11citation347DownloadsMetricsTotal Citations11Total Downloads347Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Three algorithms for processing joins on attributes of a textual type are presented and analyzed in this paper. Since such joins often involve document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ according to whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and simulation results indicate that the relative performance of these algorithms depends on the input document collections, the system characteristics and the input query. For each algorithm, the type of input document collection with which the algorithm is likely to perform well is identified.

Proximity queries that involve multiple object types are very common. In this paper, we present a parallel algorithm for answering proximity queries of one kind over object instances that lie in a one-dimensional metric space. The algorithm exploits a specialized hardware, the Dynamic Associative Access Memory chip. In most proximity queries of this kind, the number of object types is less than or equal to three and the distance d, within which object instances are required to locate to satisfy a given proximity condition, is small (dd=80e = 1). The execution time for such queries is linearly proportional to the number of object types and is independent of the size of the database. This allows numerous concurrent users to be serviced. The algorithm is extended to process 2-dimensional proximity queries efficiently. This research is supported in part by NASA under NAGW-4080 and ARO under BMDO grant DAAH04-0024. 1 1

We propose a language which can express every computable query. The language is syntactically based on the first-order predicate calculus, but semantically is interpreted as post-conditions, unlike the customary calculus query languages. The language has a capability to restrict itself to reasonable queries, accepting criteria of reasonability as a parameter.

This paper proposes a novel strategy for estimating the size of the resulting relation after an equi-join and selection using a regression model. An approximating series representing the underlying data distribution and dependency is derived from the actual data. The proposed method provides an instant and accurate size estimation by performing an evaluation of the series, with no run-time overheads in page faults and space, and with negligible CPU overhead. In contrast, the popular sampling methods incur run-time overheads in page faults (for sampling), CPU time and space. These overheads of sampling methods increase the response time of processing a query. The results of a comprehensive experimental study are also reported, which demonstrate that the estimation accuracy by the proposed method is comparable with that of the sampling methods which are believed to provide the most accurate estimation. The proposed method seems ideal for retrieval-intensive database and information systems. Since the overheads involved in deriving the approximating series are fairly moderate, we believe that this method is also an extremely competent method when moderate or periodical updates are present.

A method of variable-radix representation of numeric data is presented. The method allows compact representation of arbitrary numbers. Among its properties is that bitwise lexicographic comparison (>,>