Biomedical researchers often need to combine evidence from heterogeneous sources, including REST APIs, form-based deep web databases, semi-structured web pages, and downloadable files. Existing workflow systems support reproducible analysis but require manually specified pipelines, while LLM-based agents can issue tool calls but often lack explicit data dependencies, provenance, and controlled exploration. We present BioWeaver, an adaptive workflow orchestration system that converts natural-language biomedical questions into executable scientific workflow graphs. Each graph represents typed retrieval, refinement, and synthesis steps over a catalog of heterogeneous connectors. During execution, BioWeaver uses Progressive Data Refinement (PDR) to expand the workflow when intermediate results reveal useful follow-up records, while a composite information-gain heuristic and human-in-the-loop checkpoints control exploration depth. A unified connector abstraction supports APIs, browser-automated forms, HTML tables, and downloadable files under a common execution model. We evaluate BioWeaver on 45 queries across two biomedical question sets with gold-standard answers. Results show that BioWeaver improves overall answer quality by 6.2–14.5% over LLM baselines and by 41.4–53.8% over a ReAct agent. Ablation studies show that runtime refinement, planner knowledge, and information-gain stopping each contribute to system effectiveness. These results suggest that adaptive workflow graphs provide a practical foundation for reproducible, source-grounded biomedical data integration across heterogeneous and deep web resources.

Large Language Models (LLMs) show promise for answering general health questions, but their utility for personalized health queries is limited by their lack of access to complete individual health records and general non-compliance with HIPAA requirements. Additionally, the opacity of LLMs and their tendency to generate hallucinated responses further reduce their reliability for personalized health Question Answering (QA). In contrast, Knowledge Graphs (KGs) have proven effective for QA tasks, especially in extracting structured insights from text, but transforming free text into KGs often leads to information or context loss that can compromise answer accuracy. To overcome this challenge, we present a novel iterative and monotonic KG refinement technique that enriches knowledge representation without sacrificing contextual integrity. We formalize this approach within a mathematical framework, demonstrating that each refinement step preserves or improves answer quality. Using electronic health record data, we validate the practical feasibility of this method and introduce PerHL , a personalized health QA system built on this refinement process. Our empirical results show that the approach significantly improves answer quality for personalized health queries, marking an important step toward trustworthy, context-aware health information systems.

Discovering novel molecules with targeted properties remains a formidable challenge in materials science, often likened to finding a needle in a haystack. Traditional experimental approaches are slow, costly, and inefficient. In this study, we present an inverse design framework based on a molecular graph conditional variational autoencoder (CVAE) that enables the generation of new molecules with user-specified optical properties, particularly molar extinction coefficient (ε). Our model encodes molecular graphs, derived from SMILES strings, into a structured latent space, and then decodes them into valid molecular structures conditioned on a target ε value. Trained on a curated dataset of known molecules with corresponding extinction coefficients, the CVAE learns to generate chemically valid structures, as verified by RDKit. Subsequent Density Functional Theory (DFT) simulations confirm that many of the generated molecules exhibit the electronic structures similar to those molecules with desired ε values. We have also verified the ε values of the generated molecules using a graph neural network (GNN) and the synthesizability of those molecules using an open-source module named ASKCOS. This approach demonstrates the potential of CVAEs to accelerate molecular discovery by enabling user-guided, property-driven molecule generation – offering a scalable, data-driven alternative to traditional trial-and-error synthesis.

The rise of Large Language Models (LLMs) as powerful knowledge-processing tools has sparked a wave of innovation in tutoring and assessment systems. Despite their well-documented limitations, LLMs offer unique capabilities that have been effectively harnessed for automated feedback generation and grading in intelligent learning environments. In this paper, we introduce Project 360, an experimental intelligent tutoring system designed for teaching SQL. Project 360 leverages the concept of query equivalence to assess the accuracy of student queries, using ChatGPT's advanced natural language analysis to measure their semantic distance from a reference query. By integrating LLM-driven evaluation, Project 360 significantly outperforms traditional SQL tutoring and grading systems, offering more precise assessments and context-aware feedback. This study explores the feasibility and limitations of using ChatGPT as the analytical backbone of Project 360, evaluating its reliability for autonomous tutoring and assessment in database education. Our findings provide valuable insights into the evolving role of LLMs in education, highlighting their potential to revolutionize SQL learning while identifying areas for further refinement and improvement.

In this paper we present a method for enhancing Question Answering (QA) systems by iteratively improving Knowledge Graphs (KGs) with a focus on maintaining monotonicity in the enhancement process. We introduce a mathematical framework employing functions τ and ϕ, where τ transforms text T into a KG K, and ϕ generates an answer from T for a given question. We propose that augmenting K with domain-specific information, denoted as ΔK, leads to a more accurate approximation of the expected answer, adhering to the principle that each enhancement either maintains or improves answer quality. This concept is formalized as ϕ− 1(ϕ(T) ∪ ΔK) yielding better results than ϕ− 1(ϕ(T)). The paper elaborates on this process with practical examples, demonstrating how KG enhancements, under the constraints of monotonicity, lead to successive improvements in the Question Answering (QA) system.

Curricular advising takes up a significant amount of time for both students and faculty. Given the prerequisite graph of courses, and one's career goals, the course selections every semester should be almost deterministic. Yet, no viable tools exist to advise students automatically, or remotely. Given the rise of online learning platforms, and a significant interest in non-standard degrees, the need for an institution independent and online educational advising system has emerged. Additionally, such a system can play a significant role in a learner's career progression in a rapidly changing career landscape. In this paper, we explore the design and implementation of an advising system that can serve as a blueprint for a smart advising system.

In our quest to train well-rounded STEM students, service learning is often an afterthought in many programs even when institutional level support exists. The lack of appropriate course management platforms and systems also make it difficult for resource strapped schools to venture on their own. Service learning courses often change a student's life experience and expose them to a world of possibilities including, but not limited to transfer learning and leadership skills. In this paper, we explore the possibility of establishing an open to all online non-traditional course management platform, called EnSer, to assist instructors and colleges in engaging STEM students in service learning.

Online education platforms demand that reliable and measurable assessment and feedback generation systems be developed to ensure quality. In particular, classes requiring large software development projects need step-wise requirement specifications, verification and validation to affect proper assessment in phases. Such monitoring and assessment support is virtually non-existent in contemporary tutoring or learning management systems. In this paper, we propose a novel implementation of a team-based project management and assessment system for online database classes as an integral and vital component of a modern learning management system.

Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.

More than one third of computer science students either switch disciplines or fail the first programming class due to test/performance anxiety or learning difficulties. Personalized feedback can potentially improve both learning outcomes and retention for novice learners of computer programming. Online learning management and assessment systems have the power to allow self-paced, individualized and multi-layered learning that can be easily combined with time tested learning strategies such as pair programming, game based learning or visual programming to positively impact learning outcomes and support free-choice learning. In this paper, we introduce an online automated tutoring and assessment system, called MindReader, for introductory programming in C++. We discuss how MindReader generates personalized feedback fully automatically and prioritizes semantic error messages to avoid overwhelming the learner with a large number of signs of “failures.” We also discuss how MindReader can be used as a flexible teaching tool for introductory programming classes, and how its smart tutoring and assessment systems can improve learning.

A simple way to assess the correctness of a student’s SQL query is to compare results with a correct query for equality. Technically, the two queries will produce identical responses only if they are computationally equivalent. Until recently, establishing such equality largely tested set equality of the returned results at great computational cost with not enough confidence. In most e-Learning systems, providing feedback to improve learning has remained illusive in a setup where no semantic foundation was used to assess the query. In this paper, we present a new e-Learning platform for SQL, called ViSQL, that combines teaching, tutoring and assessment in one single system. We leverage recent development of a query equivalence solver to craft mentoring strategies using automated feedback and guidance to aid learning and scale learner support.

Computational thinking is considered critical for learning to code in K-12 education standards. The logic-first, syntax-later argument has been instrumental in promoting block-based languages such as Snap! and Scratch with limited success. In contrast, we believe that expressing the logic of computational problems in the student’s language is more convenient and natural. In such an environment, learners will experience the least impedance mismatch between their conceptual view and the target code they write. In this paper, a new natural language-based programming system, called TryPL (Try Programming in Logic), is introduced and discussed. We demonstrate that TryPL helps to identify gaps in logic and assists in validating learners’ mental models of abstract algorithms. An argument is also made as to why using a natural language-based programming environment and a learning model to teach computational thinking could be more effective and appealing to K-12 and first-year college students.

Contemporary legal digital libraries such as Lexis Nexis and WestLaw allow users to search case laws using sophisticated search tools. At its core, various forms of keyword search and indexing are used to find documents of interest. While newer search engines leveraging semantic technologies such as knowledgebases, natural language processing, and knowledge graphs are becoming available, legal databases are yet to take advantage of them fully. In this paper, we introduce an experimental legal document search engine, called Prism, that is capable of supporting legal argument based search to support legal claims.

Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed. Despite their popularity, significant limitations reduce their usability and limit applicability in novel applications. In this paper, we present a visual language, called VisFlow, for workflow orchestration using heterogeneous and distributed resources. We advance the idea that once resources are minimally described and abstracted, arbitrary workflows can be designed solely using query primitives supported in VisFlow. Its capabilities can be augmented by including computational artifacts in the form of library functions written in R, Python, and Java, or even in SQL and XQuery, making it a truly extensible system. We discuss its salient features and illustrate its capabilities using a substantial set of examples.

Despite the availability of sophisticated defense mechanisms, smart grid security breaches do happen causing serious damages. In the age of cheap and popular internet-enabled commodity products such as smart home devices, health monitors, vehicles, etc., attackers are finding increasingly more weakly-protected entry points to breach critical infrastructures such as smart grids. When fast changing attack signatures are used as a strategy to force the security apparatuses to relearn the attack properties and rethink countermeasures, critical time is lost and substantial damage can result. In this paper, we elicit an attack strategy that has the potential to breach smart grid security for which we have limited defense, and the detection of the attack takes substantially long time. We propose that in such scenarios, we deploy measures to isolate sub-networks to deny attackers access to our most valuable assets. We discuss the requirements to architect such a damage control protocol and the algorithm to partition the network using a save the best-first strategy.

The need to attract learners and improve retention in computational thinking classes has never been greater than presently. Alarmingly, research suggests that the relative popularity of computer science in K-12 and colleges is not increasing while other STEM subjects are witnessing substantial rise in enrollment. Moreover, more than one third of those who choose computer science, drop out or fail the first programming class dashing hopes of building a computationally savvy workforce for the emerging global economy. In this paper, we introduce a novel social networking system, called OpenSchool, for learners and educators alike, which is able to double as a contemporary social networking system along the lines of Facebook for instance, and serve the special needs of free-choice STEM learners, computer science learners in particular. We discuss its salient features, highlight its advantages, collaborating capabilities, and learning support for STEM, especially computational thinking, learners.

The vast majority of phylogenetic databases do not support declarative querying using which their contents can be flexibly and conveniently accessed and the template based query interfaces they support do not allow arbitrary speculative queries. They therefore also do not support query optimization leveraging unique phylogeny properties. While a small number of graph query languages such as XQuery, Cypher, and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. We develop a range of pruning aids, and propose a substantial set of query optimization strategies using these aids suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends. Possible novel optimization techniques yet to be explored are also discussed.

One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing a code and then reconstructing it from elementary knowledge and equivalence rules of elementary code segments. The approach can also be engineered to produce computable programs from conceptual and abstract algorithms as an inverse function. In this paper, we introduce the core idea behind the MindReader online assessment system that is able to understand a wide variety of elementary algorithms students learn in their entry level programming classes such as Java, C++ and Python. The MindReader system is able to assess student assignments and guide them how to develop correct and better code in real time without human assistance.

The adoption and availability of diverse application design and support platforms are making generic scientific application orchestration increasingly difficult. In such an evolving environment, higher level abstractions of design primitives are critically important using which end users have a chance to craft their own applications without a complete technical grasp of the lower level details. In this research, we introduce a novel scientific workflow design platform that supports high level tools for data integration, process description and analytics based on a visual language for naive users and advanced options for computing savvy programmers in one single platform, called VisFlow. We describe its salient features and advantages using a complex scientific application in natural resources and ecology. Video: https://youtu.be/ 2YSYVyOuuk.

Despite the recent growth in the number of phylogenetic databases, access to these wealth of resources remain largely tool or form-based interface driven. It is our thesis that the flexibility afforded by declarative query languages may offer the opportunity to access these repositories in a better way, and to use such a language to pose truly powerful queries in unprecedented ways. In this paper, we propose a substantially enhanced closed visual query language, called PhyQL, that can be used to query phylogenetic databases represented in a canonical form. The canonical representation presented helps capture most phylogenetic tree formats in a convenient way, and is used as the storage model for our PhyloBase database for which PhyQL serves as the query language. We have implemented a visual interface for the end users to pose PhyQL queries using visual icons, and drag and drop operations defined over them. Once a query is posed, the interface translates the visual query into a Datalog query for execution over the canonical database. Responses are returned as hyperlinks to phylogenies that can be viewed in several formats using the tree viewers supported by PhyloBase. Results cached in PhyQL buffer allows secondary querying on the computed results making it a truly powerful querying architecture.

Data integration continues to baffle researchers even though substantial progress has been made. Although the emergence of technologies such as XML, web services, semantic web and cloud computing have helped, a system in which biologists are comfortable articulating new applications and developing them without technical assistance from a computing expert is yet to be realized. The distance between a friendly graphical interface that does little, and a “traditional” system though clunky yet powerful, is deemed too great more often than not. The question that remains unanswered is, if a user can state her query involving a set of complex, heterogeneous and distributed life sciences resources in an easy to use language and execute it without further help from a computer savvy programmer. In this paper, we present a declarative meta-language, called VisFlow, for requirement specification, and a translator for mapping requirements into executable queries in a variant of SQL augmented with integration artifacts.

The abundance of predicted and mined but uncertain biological data show huge needs for massive, efficient and scalable curation efforts. The human expertise warranted by any successful curation enterprize is often economically prohibitive especially for speculative end user queries that may not ultimately bear fruit. So the challenge remains in devising a low cost engine capable of delivering fast but tentative annotation and curation of a set of data items that can be authoritatively validated by experts later demanding significantly small investment. The aim thus is to make a large volume of predicted data available for use as early as possible with an acceptable degree of confidence in their accuracy while the curation continues. In this paper, we present a novel approach to annotation and curation of biological database contents using crowd computing. The technical contribution is in the identification and management of trust of mechanical turks, and support for ad hoc declarative queries, both of which are leveraged to support reliable analytics using noisy predicted interactions.

The vast majority of phylogenetic databases do not support a declarative querying platform using which their contents can be flexibly and conveniently accessed. The template based query interfaces they support do not allow arbitrary speculative queries. While a small number of graph query languages such as XQuery, Cypher and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. Its deductive reasoner based implementation offers opportunities for a wide range of pruning strategies to speed up processing using query specific optimization and thus making it suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends.

The deep web is very large and diverse and queries evaluated against the deep web can provide great value. While there have been attempts at accessing the data in the deep web, these are clever "one-of'' systems and techniques. In this paper, we describe an ongoing research of a generic structured query model that can be used against the deep web. Using this query model, the contributions of a community of researchers can be combined freely, leading to a system that can be improved incrementally each time someone develops a specific novel technique to improve a particular operator.

Traditionally, biological objects such as genes, proteins, and pathways are represented by a convenient identifier, or ID, which is then used to cross reference, link and describe objects in biological databases. Relationships among the objects are often established using non-trivial and computationally complex ID mapping systems or converters, and are stored in authoritative databases such as UniGene, GeneCards, PIR and BioMart. Despite best efforts, such mappings are largely incomplete and riddled with false negatives. Consequently, data integration using record linkage that relies on these mappings produces poor quality of data, inadvertently leading to erroneous conclusions. In this paper, we discuss this largely ignored dimension of data integration, examine how the ubiquitous use of identifiers in biological databases is a significant barrier to knowledge fusion using distributed computational pipelines, and propose two algorithms for ad hoc and restriction free ID mapping of arbitrary types using online resources. We also propose two declarative statements for ID conversion and data integration based on ID mapping on-the-fly.

The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. Using VizBuilder, it is now possible to develop ad hoc complex computational biology applications at throw away costs. The underlying query language supports data integration and workflow construction almost transparently and fully automatically, using a best effort approach. Users express their application by drawing it with VizBuilder icons and connecting them in a meaningful way. Completed applications are compiled and translated as BioFlow queries for execution by the data management system LifeDB, for which VizBuilder serves as a front end. We discuss VizBuilder features and functionalities in the context of a real life application after we briefly introduce BioFlow. The architecture and design principles of VizBuilder are also discussed. Finally, we outline future extensions of VizBuilder. To our knowledge, VizBuilder is a unique system that allows visually designing computational biology pipelines involving distributed and heterogeneous resources in an ad hoc manner.

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the datagraph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

One of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. From a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve numerous high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations. We demonstrate the feasibility of our system with a practical example.

Understanding the interaction patterns among a set of biological entities in a pathway is an important exercise because it potentially could reveal the role of the entities in biological systems. Although a considerable amount of effort has been directed to the detection and mining of patterns in biological pathways in contemporary research, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways that satisfy a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways based on a novel and generic subgraph isomorphism computation technique. We cast this technique into a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways. We demonstrate that IsoKEGG is flexible enough to allow querying based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It also allows editing KGML represented query pathways and returns all possible pathways in KEGG that satisfy a given query condition that the users are able to investigate further.

The cost of reachability query computation using traditional algorithms such as depth first search or transitive closure has been found to be prohibitive and unacceptable in massive graphs such as biological interaction networks, or pathways. Contemporary solutions mainly take two distinct approaches - precompute reachability in the form of transitive closure (trade space for time) or use state space search (trade time for space). A middle ground among the two approaches has recently gained popularity. It precomputes part of the reachability information as a complex index so that most queries can be answered within a reasonable time. In this approach, the main cost now is creation of the index, and response generation using it as well as the space needed to materialize the structure. Most contemporary solutions favor a combination of these costs to be efficient for a class of applications. In this paper, we propose a hierarchical index based on graph segmentation to reduce index size without sacrificing query efficiency. We present experimental evidence to show that our approach can achieve significant space savings, and improve efficiency. We also show that our index need not be rebuilt for a large class of updates, a feature missing in all other contemporary systems.

In the last few years, several works in the literature have addressed the problem of data extraction from web pages. The importance of this problem derives from the fact that, once extracted, data can be handled in a way similar to instances of a traditional database, which in turn can facilitate application of web data integration and various other domain specific problems. In this paper, we propose a novel table extraction technique that works on web pages generated dynamically from a back-end database. The proposed system can automatically discover table structure by relevant pattern mining from web pages in an efficient way, and can generate regular expression for the extraction process. Moreover, the proposed system can assign intuitive column names to the columns of the extracted table by leveraging Wikipedia knowledge base for the purpose of table annotation. To improve accuracy of the assignment, we exploit the structural homogeneity of the column values and their co-location information to weed out less likely candidates. This approach requires no human intervention and experimental results have shown its accuracy to be promising. Moreover, the wrapper generation algorithm works in linear time.

One of the principal features of DNA microarrays is the volume of quantitative data that they generate. As a result, the major challenges in the field are how to handle, interpret and make use of these data. Currently, there are a large number of tools available which perform statistical analysis for constructing a significant gene set from the microarray data. There are others who given a set of genes, figure out the pathways, protein-protein interaction and other biological process that are represented by these genes. In this paper, we describe a comprehensive web-based tool for analyzing DNA microarray data. Our goal is to bring the database and statistical aspect of constructing a significant gene set into a single platform. We also propose a novel method for representing the global picture of interaction between pathways.

We present a novel gene finding and validation tool, called GeneScope, which is capable of finding and validating genes declaratively by autonomously integrating heterogeneous online resources. GeneScope also supports user defined concepts to articulate gene structures, and refine existing concepts toward the discovery of the intended genes. The system relies upon a new string query language called StriQL and the prototype has been implemented using the LifeDB database system. We also report the discovery of two new alternative splice variants of the human telomerase reverse transcriptase (hTERT) gene and their validation using GeneScope. We demonstrate that GeneScope’s declarativity now makes it possible for users to engage in large scale and speculative gene discovery with little or no cost. The relatively effortless discovery of the hTERT genes using GeneScope affirms this claim.

In emerging applications such as biological networks and evolving social networks, computing sub-graph isomorphism is a necessary artifact for answering many interesting questions. Efficient computation of sub-graph isomorphism, however, has remained a difficult problem in artificial intelligence and databases. In logic programming, isomorphic sub-graph search has met with limited success mainly due to representation hurdles, large memory needs and lack of a suitable procedure. In this paper, we demonstrate that a simple representation of undirected graphs enables logic based computation of isomorphic sub-graphs very efficiently. We also present a graph and query rewriting technique to compute such queries using logic programming languages such as XSB or Prolog. The space and memory requirement for this framework is extremely low even for very large networks due to the representation technique, and the manner in which the procedure functions.

Biologists are often interested to query published phylogenetic data for research purposes. PhyQL, a Web-based visual phylogenetic query engine, can be quite useful on this regard. In PhyQL, we have implemented a data model and a visual query language to interact with hierarchically classified tree elements. To hide textual query submission, PhyQL provides a design interface to build the query visually. The users can build simple to complex queries using the query operators. PhyQL separates the application layer from the data layer by a logic layer leading to reduced query tools development time. Moreover,PhyQL provides interactive tree views in radial, phylogram and dendrogram layout. It can be accessed online at http://integra.cs.wayne.edu/softwares/phyql/.

Ontologies, formal specifications of domains, have evolved in recent years as a leading tool in representing and interpreting Web data. The OntoBuilder project supports the extraction of ontologies from Web search interfaces, ranging from simple search engine forms to multiple-pages, complex reservation systems. OntoBuilder enables fully-automatic ontology matching. The use of ontologies, as opposed to relational schema or XML, as an underlying data model allows a flexible representation of metadata, that can be tailored to many different types of applications. OntoBuilder was developed using Java, which makes it portable to various platforms and operating system environments. We demonstrate OntoBuilder using an easy-to-follow example of matching car rental ontologies. The system creates ontologies of car rental Web sites on-the-fly, and combine them into a global ontology. The benefits of OntoBuilder in resolving, in an automatic manner, semantic heterogeneity, including synonyms and designer errors are highlighted.

For several obvious and practical reasons, resources needed for biological data analysis are often geographically distributed and are accessible through the Internet. Such resources usually include data repositories, analysis tools, digital documents, and so on. Such an arrangement warrants sophisticated data and process integration tools in order to design ad hoc higher level applications using these online resources. In this paper we present such a system, called the BioFlow, that exploits recent advances in workflow technology and Internet computing in order to provide support for ad hoc application development by hiding aspects related to the heterogeneity and distributive nature of the resources required by user applications. We introduce the salient features of the BioFlow system, discuss briefly its architecture and implementation issues using simple but real life applications. We demonstrate that the declarative language on which BioFlow is based makes our system quite intuitive, easy to use, effective and efficient for ad hoc application design. The approach taken in BioFlow is somewhat similar to the idea of web services in semantic web computing for biological applications.

Similar to most scientific studies, biological analyses demand a great deal of computations and simulations involving sophisticated tools that are often found geographically distributed over the Internet. A worldwide effort in genomics research has resulted in a powerful collection of publicly available sequence analysis tools. These tools often require specialized local services and domain knowledge to function correctly, rendering them unlikely candidates for integration into remote database applications. Thus, integration of heterogeneous "functions" still remains an open problem. Providing a reasonable framework for seamless integration of these tools with database query engines will enable application developers to exploit and harness the power of these effective analysis tools. In this paper, we present an integration framework for such tools by enabling access to them in a user transparent way as part of database queries. In our system, such online tools are abstracted as remote user defined functions (RUDF). An extended SQL DDL language, called the Internet Function Definition Language (IFDL), is presented for the specification and definition of RUDFs. The interface between database system and the Internet is implemented using a layer based on a language called the Hyper Text Query Language (HTQL). The separation of IFDL, DDL, HTQL and SQL DML offers several optimization opportunities and makes it possible to develop an architecture for interoperability of heterogeneous databases with RUDFs in more simple and efficient ways.

The advent of Internet and the increase in non-standard use of databases demand support for interface design primitives for ad hoc and user-friendly interface design. In this paper, we propose an interface description sub-language, called the SQLi, as a non-intrusive extension of SQL to facilitate ad hoc interface generation and data display with user preferred overriding capabilities. In support of our proposed extension and as a demonstration of its functional capabilities we introduce our system called SQL/IDL form generator that can be used as an add-on to any relational system as a query front end. We discuss the salient features of the tool and the SQLi extension through an illustrative example and argue that our proposed extension is adaptive enough to accommodate future changes and needs.

While the idea of extending XML to include object-oriented features has been gaining popularity in general, the potential of inheritance in document design has not been well recognized in contemporary research. In this paper we demonstrate that XML with dynamic inheritance aids better document designs and decreased management overheads and support increased autonomy. As an extended application, we point out that dynamic inheritance also helps effective automated web portal and ontology designs.We present an object-oriented extension to the language of XML to include dynamic inheritance and describe a middle layer that implements our system. We explain our system with several practical examples.

Recent research has focused on computing large item sets for association rule mining using SQL3 least fixpoint computation, and by exploiting the monotonic nature of the SQL3 aggregate functions such as sum and create view recursive constructs. Such approaches allow us to view mining as an ad hoc querying exercise and treat the efficiency issue as an optimization problem. We present a recursive implementation of a recently proposed least fixpoint operator for computing large item sets from object-relational databases. We present experimental evidence to show that our implementation compares well with several well-regarded and contemporary algorithms for large item set generation.

Similar to most scientific studies, biological analyses demand a great deal of computations and simulations involving sophisticated tools that are often found geographically distributed over the Internet. A worldwide effort in genomics research has resulted in a powerful collection of publicly available sequence analysis tools. These tools often require specialized local services and domain knowledge to function correctly, rendering them unlikely candidates for integration into remote database applications. Thus, integration of heterogeneous "functions" still remains an open problem. Providing a reasonable framework for seamless integration of these tools with database query engines will enable application developers to exploit and harness the power of these effective analysis tools. In this paper, we present an integration framework for such tools by enabling access to them in a user transparent way as part of database queries. In our system, such online tools are abstracted as remote user defined functions (RUDF). An extended SQL DDL language, called the Internet Function Definition Language (IFDL), is presented for the specification and definition of RUDFs. The interface between database system and the Internet is implemented using a layer based on a language called the Hypertext Query Language (HTQL). The separation of IFDL, DDL, HTQL and SQL DML offers several optimization opportunities and makes it possible to develop an architecture for interoperability of heterogeneous databases with RUDFs in more simple and efficient ways.

Querying and visualization of phylogenetic databases remain a great challenge due to their inherent complex structures. Popular phylogenetic databases such as Tree of Life and TreeBASE do not support flexible querying through query languages for the exploration of their contents. The query facility employed in these databases is usually limited to complex interfaces or is too limited to be useful for many applications. The most striking shortcoming of these systems is that they do not treat phylogenies (trees) as first citizens. In this paper, we introduce a novel visual query language for phylogenetic databases in which trees are recognized as basic units. We also introduce a Web based query interface, based on this language, for querying any tree like structure, either on the Web (e.g. Tree of Life), or in traditional relational databases (e.g. TreeBASE). As an aside, the mapping technique used in our system makes it possible to interoperate between a variety of heterogeneous phylogenetic databases. Finally, we demonstrate that the basic tree manipulation operators proposed in this paper can be used to form unlimited types of tree queries that were not possible in popular phylogenetic databases until now.

Querying and visualization of phylogenetic databases remain a great challenge due to their complex tree type semi structured nature. Naturally, successful phylogenetic databases such as the Tree of Life database at the University of Arizona are implemented as Web documents in HTML. While Web implementation of such databases facilitate the representation, and in part, visualization of their contents, querying remains an issue. The interoperability of Web-based phylogenetic databases with other similar databases such as TreeBase and RDB which are implemented using traditional database management systems, has not been possible due to the impedance mismatch between the underlying query and data representation framework. In this paper, we present a novel approach to phylogentic database management using existing database technologies without compromising potential opportunities for visualization and interoperability. We present a Web-based tool for the creation, querying and visualization of phylogenetic databases. We demonstrate the functional capabilities and strengths of our system by recreating the Tree of Life database in our system and performing queries that are not possible in the original Tree of Life database.

Although there have been several encouraging attempts at developing methods for data mining using SQL, simplicity and efficiency still remain significant impediments for further development. In this paper, we propose a significantly new approach and show that any object relational database can be mined for association rules without any restructuring or preprocessing using only basic SQL3 constructs and functions, and hence no additional machinery is necessary. In particular, we show that the cost of computing association rules for a given database does not depend on support and confidence thresholds. More precisely, the set of large items can be computed using one simple join query and an aggregation once the set of all possible meets (least fixpoint) of item set patterns in the input table is known. The principal focus of this paper is to demonstrate that several SQL3 expressions exists for the mining of association rules.

Data mining from relational databases has recently become a popular way of discovering hidden knowledge. Methods such as association rules, chi square rules, ratio rules, implication rules, etc. that have been proposed in several contexts offer complimentary choices in rule induction in this model. Other than inductive and abductive logic programming, research into data mining from knowledge bases has been almost non-existent, because contemporary methods involve inherent procedurality which is difficult to cast into the declarativity of knowledge base systems. In this paper, we propose a logic-based technique for association rule mining from declarative knowledge which does not rely on procedural concepts such as candidate generation. This development is significant as this empowers the users with the capability to explore knowledge bases by mining association rules in a declarative and ad hoc fashion.

No abstract available.

Validating hypotheses and reasoning about objects is becoming commonplace in biotechnology research. The capability to reason strengthens comparative genomics research by providing a much-needed tool to pose intelligent queries in a more convenient and declarative fashion. To be able to reason using the Genomic Query Language (GQL), we propose the idea of parameterized views as an extension of SQL's "create view" construct with an optional "with parameter" clause. Parameterizing enables traditional SQL views to accept input values and to delay the computation of the view until invoked with a "call" statement. This extension empowers users with the capability of modifying the behavior of predefined procedures (views) by sending arguments and evaluating the procedure on demand. We demonstrate that the extension is soundly based, with a parallel in Datalog. We also show that the idea of relational unification proposed in this paper empowers SQL to reason and infer in exactly the same way as an object-oriented Datalog. Thereby, it eliminates the need for cumbersome integration of database engines with deductive reasoners, as was customary in many advanced genomic database applications in the past.

Resolving the tension between the flexibilities offered by a database query language and the so-called "user friendliness" supported by a form-based or menu-driven database query interface is undoubtedly a daunting task. However, researchers believe that the flexibilities offered by a declarative query language should be fully exploited to harvest genetic solutions within the wealth of information offered by genomics and proteomics. Additionally, the need to query databases at different abstraction levels, in a set-based fashion, and to process the queries efficiently by resolving the structural and semantic disparities among the genomic databases in a user-transparent way has never been more evident than in recent years. Inspired by such needs, we have developed an SQL3-compliant database query language called the Genomic Query Language (GQL) for manipulating globally distributed genomic databases that are publicly accessible. GQL, an SQL-like language, is given a source-independent syntax and exploits a high-level global view of the underlying databases. The uniqueness of this language relies heavily on the supporting interface through which all communications take place and the interoperability of databases behind firewalls is realized. In this paper, we describe how the Web-based interface we have built for GQL makes it possible to understand the scheme of any participating database, assimilates the syntactic and semantic information into the global view, and responds to user queries. The interface is autonomous for many genomic databases and is also fully self-maintainable.