Anthony J. Bonner

Objects and views

Object-oriented databases have been introduced primarily to ease the development of database applications. However, the difficulties encountered when, for instance, trying to restructure data or integrate databases demonstrate that the models being used still lack flexibility. We claim that the natural way to overcome these shortcomings is to introduce a sophisticated view mechanism. This paper presents such a mechanism, one which allows a programmer to restructure the class hierarchy and modify the behavior and structure of objects. The mechanism allows a programmer to specify attribute values implicitly, rather than storing them. It also allows him to introduce new classes into the class hierarchy. These virtual classes are populated by selecting existing objects from other classes and by creating new objects. Fixing the identify of new objects during database updates introduces subtle issues into view design. Our presentation, mostly informal, leans on a number of illustrative examples meant to emphasize the simplicity of our mechanism.

An overview of transaction logic

Abstract This paper presents an overview of Transaction Logic —a new formalism recently introduced in Bonner and Kifer (1992, 1993) and designed to deal with the phenomenon of state changes in logic programming, databases, and AI. Transaction Logic has a natural model theory and a sound and complete proof theory. Unlike many other logics, however, it is suitable for programming procedures that accomplish state transitions in a logically sound manner. Transaction logic amalgamates such features as hypothetical and committed updates, dynamic constraints on transaction execution, nondeterminism, and bulk updates. Transaction Logic also appears to be suitable as a logical model of hitherto nonlogical phenomena, including so-called procedural knowledge in AI, and the behavior of object-oriented databases, especially methods with side effects.

Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions

Seed germination is a complex trait of key ecological and agronomic significance. Few genetic factors regulating germination have been identified, and the means by which their concerted action controls this developmental process remains largely unknown. Using publicly available gene expression data from Arabidopsis thaliana, we generated a condition-dependent network model of global transcriptional interactions (SeedNet) that shows evidence of evolutionary conservation in flowering plants. The topology of the SeedNet graph reflects the biological process, including two state-dependent sets of interactions associated with dormancy or germination. SeedNet highlights interactions between known regulators of this process and predicts the germination-associated function of uncharacterized hub nodes connected to them with 50% accuracy. An intermediate transition region between the dormancy and germination subdomains is enriched with genes involved in cellular phase transitions. The phase transition regulators SERRATE and EARLY FLOWERING IN SHORT DAYS from this region affect seed germination, indicating that conserved mechanisms control transitions in cell identity in plants. The SeedNet dormancy region is strongly associated with vegetative abiotic stress response genes. These data suggest that seed dormancy, an adaptive trait that arose evolutionarily late, evolved by coopting existing genetic pathways regulating cellular phase transition and abiotic stress. SeedNet is available as a community resource (http://vseed.nottingham.ac.uk) to aid dissection of this complex trait and gene function in diverse processes.

A logic for programming database transactions

We propose an extension of classical predicate calculus, called Transaction Logic, which provides a logical foundation for the phenomenon of state changes in logic programs and databases. Transaction Logic comes with a natural model theory and a sound and complete proof theory. The proof theory not only verifies programs, but also executes them, which makes this logic an ideal tool for declarative programming of database transactions and state-modifying logic programs. The semantics of Transaction Logic leads naturally to features whose amalgamation in a single logic has proved elusive in the past. These features include hypothetical and committed updates, dynamic constraints on transaction execution, non-determinism, and bulk updates. Finally, Transaction Logic holds promise as a logical model of hitherto non-logical phenomena, including so-called procedural knowledge in AI, and the behavior of object-oriented databases, especially methods with side effects. This paper presents the semantics of Transaction Logic and a sound and complete SLD-style proof theory for a Horn-like subset of the logic.

Hypothetical datalog: complexity and expressibility

Abstract We present an extension of Horn-clause logic which can hypothetically add and delete tuples from a database. Such logics have been discussed in the literature, but their complexities and expressibilities have remained an open question. This paper examines two such logics in the function-free, predicate case. It is shown, in particular, that augmenting Horn-clause logic with hypothetical addition increases its data-complexity from PTIME to PSPACE. When deletions are added as well, complexity increases again, to EXPTIME. We then augment the logic with negation-as-failure and develop the notion of stratified hypothetical rulebases. It is shown that negation does not increase complexity. To establish expressibility, we view the logic as a query language for relational databases. It is shown that any typed generic query that is computable in PSPACE can be expressed as a stratified rulebase of hypothetical additions. Similarly, any typed generic query that is computable in EXPTIME can be expressed as a stratified rulebase of hypothetical additions and deletions. Neither of these results assumes the data domain is linearly ordered. In this way, we establish the expressive completeness of our logics for queries in PSPACE and EXPTIME, respectively.

Workflow, transactions and datalog

‘Transaction Datalog (abbreviated TV) is a concurrent programming language that provides process modeling, database access, and advanced transactions. This paper illustrates the use of TV for specifying and simulating workflows, with examples based on the needs of a highthroughpnt genome laboratory. In addition to traditional database support, these needs include synchronization of work, cooperation between concurrent workflows, and nonserializable access to shared resources. After illustrating workflows, we use 7-1~ to explore their computational complexity in data-intensive applications. We show, for instance, that workflows can be vastly more complex than traditional database transactions, largely because concurrent processes can interact and communicate via the database (i.e., one process can read what another process writes). We then investigate the s,ources of this complexity, focusing on features for data modeling and process modeling. We show that by carefully controlling these features, the complexity of workflows can be reduced substantially. Finally, we develop a sub-language called fully bounded ‘TV that provides a practical blend of modeling features while minimizing complexity.

Sequences, Datalog and transducers

This paper develops a query language for sequence databases, such as genome databases and text databases. The language, calledSequence Datalog, extends classical Datalog with interpreted function symbols for manipulating sequences. It has both a clear operational and declarative semantics, based on a new notion called theextended active domainof a database. The extended domain contains all the sequences in the database and all their subsequences. This idea leads to a clear distinction between safe and unsafe recursion over sequences: safe recursion stays inside the extended active domain, while unsafe recursion does not. By carefully limiting the amount of unsafe recursion, the paper develops a safe and expressive subset of Sequence Datalog. As part of the development, a new type of transducer is introduced, called ageneralized sequence transducer. Unsafe recursion is allowed only within these generalized transducers. Generalized transducers extend ordinary transducers by allowing them to invoke other transducers as “subroutines.” Generalized transducers can be implemented in Sequence Datalog in a straightforward way. Moreover, their introduction into the language leads to simple conditions that guarantee safety and finiteness. This paper develops two such conditions. The first condition expresses exactly the class ofptimesequence functions, and the second expresses exactly the class of elementary sequence functions.

A Deep Non-linear Feature Mapping for Large-Margin kNN Classification

KNN is one of the most popular data mining methods for classification, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have also been used to learn powerful non-linear feature transformations, but these methods fail to scale to large datasets. In this paper, we present a scalable non-linear feature mapping method based on a deep neural network pretrained with Restricted Boltzmann Machines for improving kNN classification in a large-margin framework, which we call DNet-kNN. DNet-kNN can be used for both classification and for supervised dimensionality reduction. The experimental results on two benchmark handwritten digit datasets and one newsgroup text dataset show that DNet-kNN has much better performance than large-margin kNN using a linear mapping and kNN based on a deep autoencoder pretrained with Restricted Boltzmann Machines.

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

BackgroundArabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress.ResultsUsing in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl.ConclusionSupervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions – in this case, predictions of genes involved in stress response in plants – and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

Database Programming in Transaction Logic

This paper presents database applications of the recently proposed Transaction Logic—an extension of classical predicate logic that accounts in a clean and declarative fashion for the phenomenon of state changes in logic programs and databases. It has a natural model theory and a sound and complete proof theory, but, unlike many other logics, it allows users to program transactions. In addition, the semantics leads naturally to features whose amalgamation in a single logic has proved elusive in the past. Finally, Transaction Logic holds promise as a logical model of hitherto non-logical phenomena, including so-called procedural knowledge in AI, and the behavior of object-oriented databases, especially methods with side effects. This paper focuses on the applications of T r to database systems, including transaction definition and execution, nested transactions, view updates, consistency maintenance, bulk updates, non-determinism, sampling, active databases, dynamic integrity-constraints, hypothetical reasoning, and imperative-style programming.