The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always-important field of visual hand gesture recognition (VHGR), and delivered a wide range of applications, such as sign language understanding and human-computer interaction using cameras. Despite the large volume of research works in the field, a structured and complete survey on VHGR is still missing, leaving researchers to navigate through hundreds of papers in order to find the current state-of-the-art (SOTA). The current survey aims to fill this gap by presenting a comprehensive overview of this computer vision field. With a systematic research methodology that identifies the SOTA works and a structured presentation of the various methods, datasets, and evaluation metrics, this review aims to constitute a useful guideline for researchers, helping them to propose improvements. Specifically, this survey focuses on four fundamental questions: what are the main VHGR aspects, what are the current SOTA methods, what comparative insights can be drawn across methods and tasks, and which challenges shape future research. Starting with the methodology used to locate the related literature, the survey identifies and organizes the key VHGR approaches in a taxonomy-based format, and presents the various dimensions that affect the final method choice, such as input modality, task type, and application domain. The SOTA techniques are grouped across three primary VHGR tasks: static, isolated dynamic and continuous gesture recognition. For each task, the architectural trends and learning strategies are listed. To support the experimental evaluation of future methods in the field, the study reviews commonly used datasets and presents the standard performance metrics. Our survey concludes by identifying the major challenges in VHGR, including both general computer vision issues and domain-specific obstacles, and outlines promising directions for future research.

Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. This study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. The approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. The key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. The models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. This approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns.

The rise of financial crime that has been observed in recent years has created an increasing concern around the topic and many people, organizations and governments are more and more frequently trying to combat it. Despite the increase of interest in this area, there is a lack of specialized datasets that can be used to train and evaluate works that try to tackle those problems. This article proposes a new micro-benchmark dataset for algorithms and models that identify individuals and organizations, and their multiple writings, in news articles, and presents an approach that assists in its creation. Experimental efforts are also reported, using this dataset, to identify individuals and organizations in financial-crime-related articles using various low-billion parameter Large Language Models (LLMs). For these experiments, standard metrics (Accuracy, Precision, Recall, F1 Score) are reported and various prompt variants comprising the best practices of prompt engineering are tested. In addition, to address the problem of ambiguous entity mentions, a simple, yet effective LLM-based disambiguation method is proposed, ensuring that the evaluation aligns with reality. Finally, the proposed approach is compared against a widely used stateof-the-art open-source baseline, showing the superiority of the proposed method.

In recent years, the protection of so-called "soft targets", has become an increasingly important and challenging issue. The complexity and seriousness of this security threat have been growing exponentially, particularly with the advent of advanced technologies such as Artificial Intelligence (AI), Autonomous Vehicles (AVs), and 3D printing, especially in the context of large-scale, popular, and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square.

Fish piracy remains widespread globally despite national and international efforts. Experts estimate it accounts for about 20% of the total seafood catch worldwide. Technology is playing a key role in detecting illegal fishing, with satellite imagery and sensors being used to track vessels and monitor fishing practices. Since fishing boats broadcast their positions using a vessel tracking system, this data can be processed to detect illegal activity. This study focuses on classifying fishing vessel trajectories using only positional data. A novel trajectory representation and a Convolutional Neural Network is employed, showing promising results compared to traditional methods.

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

Abstract In the realm of online continual learning, models are expected to adapt to an ever-changing environment. One of the most persistent hurdles in this adaptation is the mitigation of a phenomenon called "Catastrophic Forgetting" (CF). This critical condition occurs when models trained on non-identically distributed data lose performance in previously learned tasks. Rehearsal methods, leveraging the ability to replay older samples, aim to address this challenge by incorporating a buffer of past training samples. However, the absence of known task boundaries complicates the adaptation of current CF mitigation methods. This paper proposes a method attuned to data stream characteristics and online model performance in a resource-constrained environment. The number of training iterations and learning rate emerges as crucial hyperparameters, impacting the efficacy and efficiency of online continual learning. Up to this point, we propose a combination of Experience Replay methodologies, a Drift Detector, and various training convergence policies, specially tailored for scenarios with unknown task boundaries. Experimental results demonstrate the effectiveness of our approach, maintaining or enhancing performance compared to baseline methods, while significantly improving computational efficiency.

Estimating the Fuel Oil Consumption (FOC) of a vessel is a critical task for the maritime industry, affecting route planning and the overall management of the vessel's operation and maintenance. Consumption is strongly coupled with the operation of the Main Engine (ME), but also with the environmental conditions (i.e., weather, ocean-energy spectrum) and the hydrodynamic features (i.e., resistance, propulsion) of the vessel. Current research shows that a multitude of features collected either from the AIS (Automatic Identification System) or on-board sensors can assist to the continuous prediction of FOC. Even when a FOC estimation model is perfectly trained on a specific vessel, its performance may degrade over time, when new weather conditions apply or when the hydrodynamics of the vessel change over time, due to fouling, aging and negligent maintenance. This work presents an online learning framework that employs a custom encoding-decoding Neural Network scheme and real-time data from various on-board sensors, to appropriately update FOC estimation models. The model is able to adapt to newly acquired data using a temporally-aware batch scheme, that samples from the initial training set using a custom auto-encoder.

The increasing use of devices with GPS capabilities has raised the need for storing and managing large amounts of spatio-temporal data, which can then be used by appropriate services and applications for extracting useful information from movement data. In parallel, it introduced the concept of multiple aspect trajectories that combine spatial, temporal, textual and social information in tandem. In order to capitalize on the social aspect of these movement data (specifically for social rankings), we formulate and address the problem of Social Spatio-Temporal-Keyword Pattern (S²KP) search over multiple aspect trajectory databases (MATDs). We propose an efficient in-DBMS k-d tree-based integrated index solution for multiple aspect trajectories that takes into account the sequential nature of trajectory data and a pattern search algorithm for this query type, implemented in Neo4j - a NoSQL graph DBMS. The overall search framework supports either an index-based spatial filtering first and then a social filtering based on social ranking and keywords, or vice versa, depending on a word frequency list. The efficacy of our proposal is demonstrated with an extensive evaluation over a real and a synthetic dataset.

Abstract\n\nThe paper outlines the sustAGE system, a smart solution that builds upon strategic technology trends, such as Internet-of-Things, machine learning and recommender systems, to support sustainable work environments and increase wellness at work and well-being with a focus on the ageing workforce. Acknowledging the interrelation of the work and private arrays for healthy ageing, the developed solution utilizes a recommendation-based approach providing personalized warnings and preventive recommendations regarding occupational risks, as well as personalized cognitive and physical training activities for the off-work context with the overall goal of maintaining Work Ability and enabling sustainable work. The piloting of the proposed solution in two critical industrial domains provides promising results towards the use of personalized recommendation-based interventions for the working context and beyond for improving workers’ occupational safety and health, performance and general well-bei ng.

Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data.

All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels’ trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.

The energy consumption of households has steadily increased over the last couple of decades. Research suggests that user behavior is the most influential factor in the energy waste of a household. Thus, there’s a need for helping consumers change their behavior to make it more energy efficient and environment friendly. In this work we propose a real-time recommender system that assists consumers in improving their household’s energy usage. By monitoring the power demand of each appliance in the household, the system detects the device status (on/off) at any moment, and using pattern mining creates a household profile comprising energy consumption patterns for different periods of the day. An intuitive UI allows users to set energy consumption goals and preferences on the appliances they’d like to save energy from. Based on the household profile, the user’s preferences and the actual power demand the system generates personalized real-time recommendations on which appliances should be turned off at a moment. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset to model and evaluate the entire process, from data preprocessing and transformation of the appliance power demand input to various pattern mining algorithms used to generate appliance usage profiles and recommendations, showing that even small changes in appliance usage behavior can lead to energy savings between 2-17%.

Graph matching is an important yet difficult task with many applications in bioinformatics, where it uncovers hidden relationships between species, network security, where it can be used for network de-anonymization, and computer vision, where it solves correspondence problems. When no side information is available, any graph matching algorithm must rely only on the structural information of the two input graphs in order to compute a mapping between the two node sets. One of the most scalable approaches for graph matching uses ideas from percolation theory, where already matched pairs ”infect” other neighbouring pairs. In the absence of any side information, such algorithms expect from the user to provide an initial set of pairs, the seeds, from which the percolation can start. In this paper, we propose DiNoiSe, a new distributed percolation graph matching algorithm, which employs the structural information of the network, but no initial seeds or any other node or edge labels. The algorithm takes advantage of the structural features of scale-free graphs (such as the ones behind social networks) and scales well for large networks, without using any additional input. We demonstrate the effectiveness of our algorithm via experiments on synthetic and real-world datasets.

In this work we face the challenge of estimating a ship's main-engine rotational speed from vessel data series, in the context of sea vessel route optimization. To this end, we study the value of different vessel data types as predictors of the engine rotational speed. As a result, we utilize speed data under a time-series view and examine how extracting locally-aware prediction models affects the learning performance. We apply two different approaches: the first utilizes clustering as a pre-processing step to the creation of many local models; the second builds upon splines to predict the target value. Given the above, we show that clustering can improve performance and demonstrate how the number of clusters affects the outcome. We also show that splines perform in a promising manner, but do not clearly outperform other methods. On the other hand, we show that spline regression combined with a Delaunay partitioning offers most competitive results.

The exponential growth in the usage of smart devices, such as smartphones, interconnected wearables etc., creates a huge amount of information to manage and many research and business opportunities. Such smart devices become a useful tool for user movement recognition, since they are equipped with different types of sensors and processors that can process sensor data and extract useful knowledge. Taking advantage of the GPS sensor, they can collect the timestamped geographical coordinates of the user, which can then be used to extract the geographical location and movement of the user. Our work, takes this analysis one step ahead and attempts to identify the user's behavior and habits, based on the analysis of user's location data. This type of information can be valuable for many other domains such as Recommender Systems, targeted/personalized advertising etc. In this paper, we present a methodology for analyzing user location information in order to identify user habits. To achieve this, we analyze user's GPS logs provided through his Google location history, we find locations that user usually spends more time, and after identifying the user's frequently preferred transportation types and trajectories, we find what type of places the user visits in a regular base (such as cinemas, restaurants, gyms, bars etc) and extract the habits that the user is most likely to have.

This work examines document clustering as a record linkage problem, focusing on named-entities and frequent terms, using several vector and graph-based document representation methods and k-means clustering with different similarity measures. The JedAI Record Linkage toolkit is employed for most of the record linkage pipeline tasks (i.e. preprocessing, scalable feature representation, blocking and clustering) and the OpenCalais platform for entity extraction. The resulting clusters are evaluated with multiple clustering quality metrics. The experiments show very good clustering results and significant speedups in the clustering process, which indicates the suitability of both the record linkage formulation and the JedAI toolkit for improving the scalability for large-scale document clustering tasks.

This study performs an Affinity Analysis ondiagnosis and prescription data in order to discover cooccurrencerelationships among diagnosis and pharmaceuticalactive ingredients prescribed to different patient groups. Theanalysis data collected during consecutive visits of 4,473 patients in a 3 years period, focused on patients suffering byhypertension and/or hypercholesterolemia and appliedassociation rule and sequential rule mining techniques. Thefindings have been validated in the specific dataset usingstatistical analysis methods. Association rule mining shows an association between gastrooesophagealreflux and the medicines prescribed forhypertension and heart diseases, which agrees with findings inthe related literature. Another interesting finding, not yet beenreported in related studies is the association between heartdiseases, gastroesophageal reflux and insulin-dependentdiabetes mellitus for patients that have both hypertension andhypercholesterolemia. Apart from the medical findings, which must be subject offurther research we propose a methodology for the analysis ofdata collected from a continuous screening process of a groupof patients. With the use of data mining techniques we are ableto extract and formulate the potential research questions, which are then validated using statistical methods and can alsobe validated in larger population studies.

The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in order to smartly track and identify user's activity, synchronizes with the user's calendar, recommends personalized workout sessions based on the user's preferences, fitness goals, and availability. Moreover, PRO-Fit integrates with the user's social network and recommends “fitness buddies” with similar preferences and availability.

Online advertisements are a major source of profit and customer attraction for web-based businesses. In a successful advertisement campaign, both users and businesses can benefit, as users are expected to respond positively to special offers and recommendations of their liking and businesses are able to reach the most promising potential customers. The extraction of user preferences from content provided in social media and especially in review sites can be a valuable tool both for users and businesses.

Smartphones are nowadays used for recognizing people's daily activities and habits, by collecting and analysing user activity information in real-time. In order to demonstrate this methodology, we have developed GPSTracker1a prototype application for Android phones, which collects position, speed, altitude and time information and performs real-time classification of user's movement. The GPSTracker application also uses geo-location information abouts Points Of Interest (POIs) such as bus or metro routes, parks and stadiums in order to improve the set of features used for the classification of a type of movement. In this work, we use evolutionary algorithms, in order to reduce the number of samples required for training our classifier, without loosing in classification accuracy. The resulting model, a) is able to provide personalized solutions, tuned to each individual users movement abilities, b) better adapts to unbalanced training data, due to the generation of training samples from the existing ones, c) performs an initial sampling of the training data, which reduces requirements for computational resources and improves the classification performance.

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these connections, and provides personalized user recommendations to the network members.

Traditional document indexing techniques store documents using easily accessible representations, such as inverted indices, which can efficiently scale for large document sets. These structures offer scalable and efficient solutions in text document management tasks, though, they omit the cornerstone of the documents' purpose: meaning. They also neglect semantic relations that bind terms into coherent fragments of text that convey messages. When semantic representations are employed, the documents are mapped to the space of concepts and the similarity measures are adapted appropriately to better fit the retrieval tasks. However, these methods can be slow both at indexing and retrieval time. In this paper we propose SemaFor, an indexing algorithm for text documents, which uses semantic spanning forests constructed from lexical resources, like Wikipedia, and WordNet, and spectral graph theory in order to represent documents for further processing.

Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analyzed across different dimensions(e.g., author, year, venue, topic) and can be exploited in multiple ways. The representation and visualization of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualization tool from the biological domain, we are able to provide comprehensive visualizations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and their effect in the process of information propagation in the virtual world. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between local and global influence and compare suggestions provided by locally or globally trusted users.

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

With the unstoppable growth of the world wide Web, the great success of Web search engines, such as Google and AltaVista, users now turn to the Web whenever looking for information. However, many users are neophytes when it comes to computer science, yet they are often specialists of a certain domain. These users would like to add more semantics to guide their search through world wide Web material, whereas currently most search features are based on raw lexical content. We show how the use of the incoming links of a page can be used efficiently to classify a page in a concise manner. This enhances the browsing and querying of Web pages. We focus on the tools needed in order to manage the links and their semantics. We further process these links using a hierarchy of concepts, akin to an ontology, and a thesaurus. This work is demonstrated by an prototype system, called THESUS, that organizes thematic Web documents into semantic clusters. Our contributions are the following: 1) a model and language to exploit link semantics information, 2) the THESUS prototype system, 3) its innovative aspects and algorithms, more specifically, the novel similarity measure between Web documents applied to different clustering schemes (DB-Scan and COBWEB), and 4) a thorough experimental evaluation proving the value of our approach.

Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user's navigational behavior. Integrating usage data with content, structure or user profile data enhances the results of the personalization process. In this paper, we present SEWeP, a system that makes use of both the usage logs and the semantics of a Web site's content in order to personalize it. Web content is semantically annotated using a conceptual hierarchy (taxonomy). We introduce C-logs, an extended form of Web usage logs that encapsulates knowledge derived from the link semantics. C-logs are used as input to the Web usage mining process, resulting in a broader yet semantically focused set of recommendations.

Many organizations and enterprises establish distributed working environments, where different users need to exchange information based on a common model. XML is widely used to facilitate this information exchange. The extensibility of XML allows the creation of generic models that integrate data from different sources. For these tasks, several applications are used to import and export information in XML format from the data repositories. In order to support this process for relational repositories we developed the X-Database system. The base of this system is an XML-Schema file that describes the logical model of interchanged information. Initially, the system analyses the syntax of the XML-Schema file and generates the relational database. Then it handles the decomposition of valid XML files according to that Schema and the composition of XML documents from the information in the database. Finally the system offers a flexible mechanism for modifying and querying database contents using only valid XML documents, which are validated over the XML-Schema file's rules.

We present a system that aims at increasing the flexibility and accuracy of information retrieval tasks in the World Wide Web. The system offers extended searching capabilities by enriching information related to hyperlinks between documents. It offers to document authors the ability to attach additional information to hyperlinks and also provides suggestions on the information to be attached. In an effort to increase the integrity of hyperlink information, a conversion module extracts, from the pages, metadata concerning the linked documents as well as the link itself. The hyperlink metadata is appended to the original document metadata and an XML document is created. Another module allows the end users to query the XML-document base, taking advantage of the enhanced hyperlink information. We present an overview of the system and the solutions it provides in problems found to similar approaches.