Computation over large data sets is now a critical part of most natural and social sciences. As such, having ways to capture the provenance and lineage of the data is a sine qua non for not just reproducibility, but also for these computational approaches to inform public policy. Standards like PROV-O, RO-Crate, IEEE 2791, and ISO-23494 define provenance for computational processes but lack detailed specifications for describing entities beyond simple URIs or linear hash sums. This is usually sufficient for smaller datasets with the size of a gigabyte or two. However, contempory datasets in domains like the life sciences and high energy physics often exceed tens of terabytes in size. For these datasets, linear hash sums are no longer feasible. In addition these large datasets are frequently copied around to bring them closer to computational resources, limiting URL based tracking.In this article, we introduce Crystalia, a novel, flexible, and parallelizable method for computing dataset descriptions using Merkle trees, represented as Knowledge Graphs. Our solution enables efficient tracking of dataset versions and transformations by capturing reproducibility information in a scalable manner. Leveraging Knowledge Graphs facilitates annotation, exchange, and integration with existing provenance standards.

The widespread use of big data across various sectors has brought significant privacy concerns, particularly when sensitive information is shared or analyzed. Regulations like GDPR and HIPAA impose strict controls on handling data, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers a promising solution, enabling the creation of artificial datasets that mirror real-world patterns without exposing sensitive information. For instance, synthetic data can simulate patient records or network flows for training machine learning models to conduct research without violating privacy laws. However, traditional synthetic data generation methods often fail to capture complex, implicit rules that relate different elements of the data and are essential in specific domains like healthcare. While these methods might replicate explicit patterns from the training data, they often overlook domain-specific rules that are not directly stated but are critical for maintaining realism and utility. For example, prescription guidelines, such as avoiding certain medications for patients with specific conditions or preventing harmful drug interactions, may not be explicitly represented in the original data. Synthetic data generated without accounting for these implicit rules can lead to medically inappropriate or unrealistic patient profiles. To address these limitations, we propose a framework called Context-Aware Differentially Private Generative Adversarial Network (ContextGAN). Our framework integrates domain-specific rules using a constraint matrix that explicitly encodes both explicit and implicit domain knowledge. The constraint-aware discriminator evaluates synthetic data against these rules, ensuring the generated data adheres to domain constraints. Furthermore, the discriminator is differentially private, ensuring privacy preservation by protecting sensitive details from the original data. We validate ContextGAN across multiple domains, including healthcare, security, and finance, demonstrating that it produces high-quality synthetic data that respects domain-specific rules while preserving privacy. Our results show that ContextGAN significantly improves the realism and utility of synthetic data by enforcing domain constraints, making it suitable for use in scenarios requiring both compliance with explicit patterns and implicit rules, all under strict privacy guarantees.

Narrative construction is the process of representing disparate event information into a logical plot structure that models an end to end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing event information into comprehensive intelligence reports. Manual intelligence report generation is often prone to challenges such as integrating dynamic event information, writing fine-grained queries, and closing information gaps. This motivates the development of a system that retrieves and represents critical aspects of events in a form that aids in automatic generation of intelligence reports.

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Synthetic data that retains the statistical properties of the original data but does not include actual individuals’ information provides a suitable alternative to sharing sensitive datasets. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats, such as re-identification and linkage issues, and secure data based on applicable regulatory policy rules.

Cybersecurity Knowledge Graphs (CKGs) help in aggregating information about cyber-events. CKGs combined with reasoning and querying systems such as SPARQL enable security researchers to look up information about past cyberevents that is helpful in understanding future cyber-events or drawing similarity with a known cyber-event recorded in a CKG. CKGs have assertions in the form of semantic triples. The triples describe a relationship between a subject and object, both of which are cybersecurity entities. The quality of information present in the CKG depends on the data source. Since data sources can have varying degrees of reliability, we need a score that should help us benchmark the veracity of the CKG assertions. Verifying the information asserted in the CKG is a challenging task. In this paper, we describe a novel method that associates a score with the semantic triples asserted in the CKG using deep learning. We use semantic triples that we know are correct, in a supervised machine learning algorithm that produces the output for each relationship. In particular, we use Graph Convolutional Neural Networks (GCN) on a dataset of CKGs that can be used to ascertain the scores for each semantic triple.

Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat Intelligence (CTI). In the cybersecurity domain, such CTI is stored in Cybersecurity Knowledge Graphs (CKG). We enhance the exiting CKG model to incorporate intelligence provenance and fuse provenance graphs with CKG. This process includes modifying traditional approaches to entity and relation extraction. CTI data is considered vital in securing our cyberspace. Knowledge graphs containing CTI information along with its provenance can provide expertise to dependent Artificial Intelligence (AI) systems and human analysts.

We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is providing the security community with an initial fine-tuned BERT model that can perform a variety of cybersecurity-specific downstream tasks with high accuracy and efficient use of resources. We create a cybersecurity corpus from open-source unstructured and semi-unstructured Cyber Threat Intelligence (CTI) data and use it to fine-tune a base BERT model with Masked Language Modeling (MLM) to recognize specialized cybersecurity entities. We evaluate the model using various downstream tasks that can benefit modern Security Operations Centers (SOCs). The fine-tuned CyBERT model outperforms the base BERT model in the domain-specific MLM evaluation. We also provide use-cases of CyBERT application in cybersecurity based downstream tasks.

With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked datasets for high-resource languages (English, French, and German) in specific domains like restaurants, hotels or electronic goods but not in low-resource languages like Nepali. In this paper, we present our work to create a dataset for the targeted aspect-based sentiment analysis in the social media domain, set up a dataset benchmark and evaluate using various machine learning models. The dataset comprises of code-mixed and code-switched comments extracted from Nepali YouTube videos. We present convincing baselines using a multilingual BERT model for the Aspect Term Extraction task and BiLSTM model for the Sentiment Classification Task achieving 57.978% and 81.60% F1 score respectively.

In recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.

Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber-entities or key expressions can be used to model a cyber-attack described in an open-source text. A large number of general-purpose NER algorithms have been published that work well in text analysis. These algorithms do not perform well when applied to the cybersecurity domain. In the field of cybersecurity, the open-source text available varies greatly in complexity and under-lying structure of the sentences. General-purpose NER algorithms can misrepresent domain-specific words, such as "malicious" and "javascript". In this paper, we compare the recent deep learning-based NER algorithms on a cybersecurity dataset. We created a cybersecurity dataset collected from various sources, including "Microsoft Security Bulletin" and "Adobe Security Updates". Some of these approaches proposed in literature were not used for Cybersecurity. Others are innovations proposed by us. This comparative study helps us identify the NER algorithms that are robust and can work well in sentences taken from a large number of cybersecurity sources. We tabulate their performance on the test set and identify the best NER algorithm for a cybersecurity corpus. We also discuss the different embedding strategies that aid in the process of NER for the chosen deep learning algorithms.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

Security Analysts that work in a 'Security Operations Center' (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat intelligence sources, like text descriptions about cyber-attacks, can be stored in a structured fashion in a cybersecurity knowledge graph. A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple contains two cybersecurity entities with a relationship between them. In this work, we propose a system to create semantic triples over cybersecurity text, using deep learning approaches to extract possible relationships. We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack.

In order to secure vital personal and organizational system we require timely intelligence on cybersecurity threats and vulnerabilities. Intelligence about these threats is generally available in both overt and covert sources like the National Vulnerability Database, CERT alerts, blog posts, social media, and dark web resources. Intelligence updates about cybersecurity can be viewed as temporal events that a security analyst must keep up with so as to secure a computer system. We describe CyberTwitter, a system to discover and analyze cybersecurity intelligence on Twitter and serve as a OSINT (Open-source intelligence) source. We analyze real time information updates, in form of tweets, to extract intelligence about various possible threats. We use the Semantic Web RDF to represent the intelligence gathered and SWRL rules to reason over extracted intelligence to issue alerts for security analysts.

Ensuring privacy of Big Data managed on the cloud is critical to ensure consumer confidence. Cloud providers publish privacy policy documents outlining the steps they take to ensure data and consumer privacy. These documents are available as large text documents that require manual effort and time to track and manage. We have developed a semantically rich ontology to describe the privacy policy documents and built a database of several policy documents as instances of this ontology. We next extracted rules from these policy documents based on deontic logic which can be used to automate management of data privacy. In this paper we describe our ontology in detail along with the results of our analysis of privacy policies of prominent cloud services.

To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our prototype system that uses a Hadoop cluster to extract knowledge from unstructured legal text documents. For this prototype we have considered publicly available SLA/terms of service documents of various cloud providers. We use established natural language processing techniques in parallel to speed up cloud legal knowledge base creation. Our system considerably speeds up knowledge base creation and can also be used in other domains that have unstructured data.

Scientists and casual users need better ways to query RDF databases or Linked Open Data. Using the SPARQL query language requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used, and URIs for entities of interest. Natural language query systems are a powerful approach, but current techniques are brittle in addressing the ambiguity and complexity of natural language and require expensive labor to supply the extensive domain knowledge they need. We introduce a compromise in which users give a graphical "skeleton" for a query and annotates it with freely chosen words, phrases and entity names. We describe a framework for interpreting these "schema-agnostic queries" over open domain RDF data that automatically translates them to SPARQL queries. The framework uses semantic textual similarity to find mapping candidates and uses statistical approaches to learn domain knowledge for disambiguation, thus avoiding expensive human efforts required by natural language interface systems. We demonstrate the feasibility of the approach with an implementation that performs well in an evaluation on DBpedia data.

Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMImax, that augments PMI with information about a word's number of senses. The coefficients of PMImaxare determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the application of automatic thesaurus generation and in two word similarity benchmark tasks: human similarity ratings and TOEFL synonym questions. PMImaxachieves a correlation coefficient comparable to the best knowledge-based approaches on the Miller-Charles similarity rating data set.

We need better ways to query large linked data collections such as DBpedia. Using the SPARQL query language requires not only mastering its syntax but also understanding the RDF data model, large ontology vocabularies and URIs for denoting entities. Natural language interface systems address the problem, but are still subjects of research. We describe a compromise in which non-experts specify a graphical query "skeleton" and annotate it with freely chosen words, phrases and entity names. The combination reduces ambiguity and allows the generation of an interpretation that can be translated into SPARQL. Key research contributions are the robust methods that combine statistical association and semantic similarity to map user terms to the most appropriate classes and properties in the underlying ontology.

There is an increasing penetration of smart phones within enterprises. Most smart phone users now run both enterprise as well as personal applications simultaneously on their phones. However, most of the personal apps that are downloaded from public market places are hardly tested for enterprise grade security, and there have been instances of malware appearing in public markets that steal sensitive user information. Smart phone platforms such as Android require users to explicitly provide permissions to applications at install time, yet lack run time monitoring of permission usage by applications. In this paper, we present a framework for the run time enforcement of privacy policies on smart phones, in particular, protecting the privacy of enterprise data on smart phones. Our privacy policies are defined in terms of permissible information flows on the phone during different contexts. This arms users with finer grained control over information access by different applications. In our policy framework, an information flow is defined based on the entities involved in the corresponding inter-process communication(IPC) viz, the caller, callee and the associated IPC data. The information flow policy specifies the conditions under which an IPC flow may be permitted (or denied). Our system tracks information flows at run time and enforces that only flows satisfying all the current policies are permitted on the phone. We describe the design and implementation of our policy based framework in Android, and present performance evaluation results measuring the overhead imposed by our framework.

Mobile Ad-hoc Networks (MANETs) are extremely susceptible to various misbehaviors and a variety of trust management schemes have been proposed to detect and mitigate them. Most schemes rely on a set of pre-defined weights to determine how the extent of each misbehavior is used to evaluate the trustworthiness. However, due to the extremely dynamic nature of MANETs, it is not possible to determine a set of weights that are appropriate for all contexts. In this paper, an Automated Trust Management (ATM) system is described for MANETs that uses a support vector machine classifier to detect malicious MANET nodes. The ATM scheme is resilient to attempts by a malicious MANET node to hide its nature by varying its misbehavior patterns over time. The performance of the ATM scheme is evaluated via an extensive simulation study and compared with existing approaches.

Nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets to enable communication between other nodes that are not in radio range with each other. However, whether for selfish or malicious reasons, a node may fail to cooperate during the network operations or even attempt to disturb them, both of which have been recognized as misbehaviors. Various trust management schemes have been studied to assess the behaviors of nodes so as to detect and mitigate node misbehaviors inMANETs. Most of existing schemes model a node's trustworthiness along a single dimension, combining all of the available evidence to calculate a single, scalar trust metric. A single measure, however, may not be expressive enough to adequately describe a node's trustworthiness in many scenarios. In this paper, we describe a multi-dimensional framework to evaluate the trustworthiness of MANET node from multiple perspectives. Our scheme evaluates trustworthiness from three perspectives: collaboration trust, behavioral trust, and reference trust. Different types of observations are used to independently derive values for these three trust dimensions. We present simulation results that illustrate the effectiveness of the proposed scheme in several scenarios.

Recent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very thoughts and interests. In this context, micro-blogging has been widely adopted by users as an effective means to capture and disseminate their thoughts and actions to a larger audience on a daily basis. Interestingly, daily chatters of a user obtained from her micro-blogs offer a unique information source to analyze and interpret her context in real-time - i.e. interests, intentions,and activities. In this paper, we gather data from the public timeline of Twitter spanning across ten worldwide cities over a period of four weeks. We use this dataset to (a) explore how users express interests in real-time through micro-blogs, and (b) understand how text mining techniques can be applied to interpret real-time context of a user based on her tweets. Initial findings reported herein suggest that social media sites like Twitter constitute a promising source for extracting user context that can be exploited by novel social networking applications.

We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-of-words approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems.

Mobile Ad-hoc NETworks (MANETs) are known to be vulnerable to a variety of attacks due to lack of central authority or fixed network infrastructure. Many security schemes have been proposed to identify misbehaving nodes. Most of these security schemes rely on either a predefined threshold, or a set of well-defined training data to build up the detection mechanism before effectively identifying the malicious peers. However, it is generally difficult to set appropriate thresholds, and collecting training datasets representative of an attack ahead of time is also problematic. We observe that the malicious peers generally demonstrate behavioral patterns different from all the other normal peers, and argue that outlier detection techniques can be used to detect malicious peers in ad hoc networks. A problem with this approach is combining evidence from potentially untrustworthy peers to detect the outliers. In this paper, an outlier detection algorithm is proposed that applies the Dempster-Shafer theory to combine observation results from multiple nodes because it can appropriately reflect uncertainty as well as unreliability of the observations. The simulation results show that the proposed scheme is highly resilient to attackers and it can converge stably to a common outlier view amongst distributed nodes with a limited communication overhead.

Social Network Analysis has emerged as a key paradigm in modern sociology, technology, and information sciences. The paradigm stems from the view that the attributes of an individual in a network are less important than their ties (relationships) with other individuals in the network. Exploring the nature and strength of these ties can help understand the structure and dynamics of social networks and explain real-world phenomena, ranging from organizational efficiency to the spread of information and disease.

With the ever-growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Existing approaches for telecom business intelligence have almost solely focused on the individual behavior of customers. In this paper, we use the call detail records of a mobile operator to construct call graphs, that is, graphs induced by people calling each other. We determine the structural properties of these graphs and also introduce the Treasure-Hunt model to describe the shape of mobile call graphs. Moreover, we determine how the structure of these call graphs evolve over time. Finally, since short messaging service (SMS) is becoming a preferred mode of communication among many sections of the society, we study the properties of the SMS graph. Our analysis indicates several interesting similarities and differences between the SMS graph and the corresponding call graph. We believe that our analysis techniques can allow telecom operators to better understand the social behavior of their customers and potentially provide major insights for designing effective incentives.

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).

We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over 15 years, uses a specially constructed NLP-oriented ontology and an ontological-semantic lexicon to translate English text into a custom ontology- motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental Web service that monitors RSS news sources, processes the summaries of the news stories, and publishes a structured representation of the meaning of the text in the news story.

With ever growing competition in telecommunications markets, operators have to increasingly rely on business intelligence to offer the right incentives to their customers. Toward this end, existing approaches have almost solely focussed on the individual behaviour of customers. Call graphs, that is, graphs induced by people calling each other, can allow telecom operators to better understand the interaction behaviour of their customers, and potentially provide major insights for designing effective incentives.In this paper, we use the Call Detail Records of a mobile operator from four geographically disparate regions to construct call graphs, and analyse their structural properties. Our findings provide business insights and help devise strategies for Mobile Telecom operators. Another goal of this paper is to identify the shape of such graphs. In order to do so, we extend the well-known reachability analysis approach with some of our own techniques to reveal the shape of such massive graphs. Based on our analysis, we introduce the Treasure-Hunt model to describe the shape of mobile call graphs. The proposed techniques are general enough for analysing any large graph. Finally, how well the proposed model captures the shape of other mobile call graphs needs to be the subject of future studies.

In this paper, we describe a Semantic Web application that detects Conflict of Interest (COI) relationships among potential reviewers and authors of scientific papers. This application discovers various 'semantic associations' between the reviewers and authors in a populated ontology to determine a degree of Conflict of Interest. This ontology was created by integrating entities and relationships from two social networks, namely "knows," from a FOAF (Friend-of-a-Friend) social network and "co-author," from the underlying co-authorship network of the DBLP bibliography. We describe our experiences developing this application in the context of a class of Semantic Web applications, which have important research and engineering challenges in common. In addition, we present an evaluation of our approach for real-life COI detection.

Swoogle is a crawler-based indexing and retrieval system for the Semantic Web. It extracts metadata for each discovered document, and computes relations between documents. Discovered documents are also indexed by an information retrieval system which can use either character N-Gram or URIrefs as keywords to find relevant documents and to compute the similarity among a set of documents. One of the interesting properties we compute is ontology rank, a measure of the importance of a Semantic Web document.

This paper presents a framework to address new data management challenges introduced by data-intensive, pervasive computing environments. These challenges include a spatio-temporal variation of data and data source availability, lack of a global catalog and schema, and no guarantee of reconnection among peers due to the serendipitous nature of the environment. An important aspect of our solution is to treat devices as semi-autonomous peers guided in their interactions by profiles and context. The profiles are grounded in a semantically rich language and represent information about users, devices and data described in terms of “beliefs”, “desires”, and “intentions”. We present a prototype implementation of this framework over combined Bluetooth and Ad-Hoc 802.11 networks, and present experimental and simulation results that validate our approach and measure system performance.

We describe an approach to retrieval of documents that contain of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statements provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup together will significantly improve retrieval performance. Our approach allows inferencing to be done over this information at several points: when a document is indexed, when a query is processed and when query results are evaluated.

We address issues related to mobility, personalization and asynchronous operation to support efficient access to the World Wide Web across weakly connected or low bandwidth wireless links. WWW provides a large globally, distributed computing and information access infrastructure. With the large amount of accessible information came the problem of information overload, making the process of finding content of interest a difficult issue. This is further exacerbated in mobile environments because of the well known resource constraints. We have developed MobileIQ, a distributed system and service infrastructure that can offer personalized mobile information access in a wireless network environment. MobileIQ addresses challenges unique to a mobile environment such as mobility management and disconnection management, in addition to issues common to all environments: content personalization, bandwidth utilization, download latencies, and user privacy. We also demonstrate how this architecture may be used to provide location-based services to users of the MobileIQ system.

The pervasive computing environments of the near future will involve the interactions, coordination and cooperation of numerous, casually accessible, and often invisible computing devices. These devices, whether carried on our person or embedded in our homes, businesses and classrooms, will connect via wireless and wired links to one another and to the global networking infrastructure. The result will be a networking milieu with a new level of openness. The localized and dynamic nature of their interactions raises many new issues that draw on and challenge the disciplines of agents, distributed systems, and security. This paper describes recent work by the UMBC Ebiquity research group which addresses some of these issues.