Blockchain technology has emerged as a transformative force across various sectors, especially in enhancing collaborative scientific workflows. This paper delves into the unique challenges and opportunities associated with integrating blockchain into these workflows. We conduct a comprehensive analysis of recent literature to identify key themes, categorize different approaches, and assess the potential of blockchain to improve data integrity, provenance, and collaborative research efforts among diverse stakeholders. Through this exploration, we aim to provide an insightful overview of the current landscape and propose directions for future research focused on the role of blockchain in facilitating effective collaboration in scientific endeavors.

Scientific workflow has become a popular cyberinfrastructure paradigm to accelerate scientific discoveries by enabling scientists to formalize and structure complex scientific processes. With the recent success of deep learning models in many scientific applications, there is a rising need for infrastructure-level support for deep learning technologies in scientific workflow cyberinfrastructures. However, current scientific workflow cyberinfrastructures and GPU-enabled deep learning frameworks are developed separately, neither alone can be a satisfactory choice. In this paper, We propose the Deep-Learning-as-a-Workflow approach in DATAVIEW, which for the first time incorporates native infrastructure level support for GPU-enabled deep learning in a scientific workflow management system and enables the fast training and execution of neural networks as workflows (NNWorkflows) leveraging various types of GPU resource configurations. Our experiments demonstrate the salient usability feature of DATAVIEW in providing seamless infrastructure-level support to both scientific and deep learning workflows in one system, while delivering competitive (better in most cases) learning efficiency compared to the conventional implementations based on Keras.

Research integrity is crucial to ensuring the trustworthiness of scientific discoveries. This work is aimed at detecting misbehaviors targeting scientific workflows, which are computing paradigms widely used to facilitate scientific collaborations across multiple geographically distributed research sites. We develop a new system called RAMP(Real-Time Aggregated Matrix Profile) for real-time anomaly detection in scientific workflow systems. RAMP builds upon an existing time series data analysis technique called Matrix Profile to detect anomalous distances among subsequences of event streams collected from scientific workflows in an online manner. Using an adaptive uncertainty function, the anomaly detection model is dynamically adjusted to prevent high false alarm rates. RAMP can incorporate user feedback on reported anomalies and modify model parameters to improve anomaly detection accuracy. Our experimental results from applying RAMP to the logs generated by DATAVIEW, a scientific workflow platform, show that RAMP is able to identify a varied range of anomalies with high accuracy for both interleaved and non-interleaved workflow executions in real time.

Early intervention in autism, although deemed as essential, has high variance in the outcome attained, partially due to complex interaction between multitude of factors and variables involved, and the lack of systematic study to untangle their influences in the outcome. Therefore, pairing set of interventions with an individual children to cater for their need remains highly challenging. From the perspective of parents, unknown factors emanate from their unfamiliarity with what interventions are out there and why. From the perspective of caregivers, it is critical to understand unique attributes of the individual children develop over time. There is a scarcity of exploration of interactions between attributes specific to a child, family characteristics and therapeutic, medical and educational services. In this research, we aim to bridge the gap. In this study, we identify predictive features pertaining to each individual child and how they interact responding to different interventions and services. We have studied temporal data and model improvement/regression outcomes at different timestamped milestones and overlayed a model to aid parents and caregivers in coming up with pragmatic intervention plan. We propose a scientific workflow to automate the modeling process and rely on DATAVIEW to guarantee computational reproducibility and data fidelity. We use data collected by SFARI dataset for evaluation. To the best of our knowledge, this is first-time amalgamation between the Autism Health informatics community and the Workflow community; and this is the first-time study that combines prediction methods applied on Autism Spectrum Disorder (ASD) Phenotype data to provide guidance to parents and caregivers.

Big data is fast becoming a ubiquitous term in both academia and industry and there is a strong need for new data-centric workflow tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. On the other hand, the unbound resource leasing capability foreseen in the cloud facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. In the data-centric workflow environment, scheduling data processing tasks onto appropriate resources are often driven by the constraints provided by the users. Enforcing a constraint while executing the workflow in the cloud adds a new optimization challenge on how to meet the objective while satisfying the given constraint. In this paper, we propose a new Big dAta woRkflow schEduler uNder budgeT constraint known as BARENTS that supports high-performance workflow scheduling in a heterogeneous cloud computing environment with a single objective to minimize the workflow makespan under a provided budget constraint. Our case study and experiments show the competitive advantages of our proposed scheduler. The proposed BARENTS scheduler is implemented in a new release of DATA VIEW, one of the most usable big data workflow systems in the community.

Workflow makespan is the total execution time for running a workflow in the Cloud. The workflow makespan significantly depends on how the workflow tasks and datasets are allocated and placed in a distributed computing environment such as Clouds. Incorporating data and task allocation strategies to minimize makespan delivers significant benefits to scientific users in receiving their results in time. The main goal of a task placement algorithm is to minimize the total amount of data movement between virtual machines during the execution of the workflows. In this paper, we do the following: 1) formalize the task placement problem in big data workflows; 2) propose a task placement strategy (TPS) that considers both initial input datasets and intermediate datasets to calculate the dependency between workflow tasks; and 3) perform extensive experiments in the distributed environment to demonstrate that the proposed strategy provides an effective task distribution and placement tool.

The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.

Provenance, the metadata that pertains to the derivation history of a data product, has become increasingly important in scientific workflow environments. In many cases, both data products and their provenance can be sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose i) a formalization of scientific workflow provenance as the basis for querying and access control; ii) a security specification mechanism for provenance at various granularity levels and the derivation of a full security specification based on inheritance, overriding, and conflict resolution rules; iii) a formalization of security views that are derived from a scientific workflow run provenance for different roles of users; and iv) a framework that integrates abstraction views and security views such that a user can examine provenance at different abstraction levels while respecting the security policy prescribed for her. We have developed the SecProv prototype to validate the effectiveness of our approach.

Relational technology has shown to be very useful for scalable Semantic Web data management. Numerous researchers have proposed to use RDBMSs to store and query voluminous RDF data using SQL and RDF query languages. In this article, we study how RDF queries with the socalled well-designed graph patterns and nested optional patterns can be efficiently evaluated in an RDBMS. We propose to extend relational databases with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional patterns of well-designed graph patterns. We design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm (NL-NOJ); (2) sortmerge NOJ algorithm (SM-NOJ); and (3) simple hash NOJ algorithm (SH-NOJ). Based on a real-life RDF dataset, we demonstrate the efficiency of our algorithms by comparing them with the corresponding left outer join implementations and explore the effect of join selectivity on the performance of our algorithms.

One of the major challenges in the post-genomic era is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites (motifs) within the genomes. To discover the relationship between the motifs and changes in gene expression, we propose a new algorithm, co-miner (correlation miner). Correlation rules are generated based on the expression profiles of genes with significant expression change through the time course of gene expression. Thus, we may consider the change in gene expression to be causatively associated with the transcription binding sites in the upstream sequences. In addition, we introduce partition and constraint pushing techniques to improve the performance and demonstrate their effectiveness by our experiments. By applying co-miner to a yeast dataset, the relationships between motifs and gene expression revealed by co-miner are confirmed in the literature.

The development of the Semantic Web, the next-generation Web, greatly relies on the availability of ontologies and powerful annotation tools. However, there is a lack of ontology-based annotation tools for linguistic multimedia data. Existing tools either lack ontology support or provide limited support for multimedia. To fill the gap, we present an ontology-based linguistic multimedia annotation tool, OntoELAN, which features: (1) the support for OWL ontologies; (2) the management of language profiles, which allow the user to choose a subset of ontological terms for annotation; (3) the management of ontological tiers, which can be annotated with language profile terms and, therefore, corresponding ontological terms; and (4) storing OntoELAN annotation documents in XML format based on multimedia and domain ontologies. To our best knowledge, OntoELAN is the first audio/video annotation tool in the linguistic domain that provides support for ontology-based annotation. It is expected that the availability of such a tool will greatly facilitate the creation of linguistic multimedia repositories as islands of the Semantic Web of language engineering.

Many transaction processing applications execute at isolation levels lower than SERIALIZABLE in order to increase throughput and reduce response time. However, the resulting schedules might not be serializable and, hence, not necessarily correct. The semantics of a particular application determines whether that application will run correctly at a lower level and, in practice, it appears that many applications do. The decision to choose an isolation level at which to run an application and the analysis of the correctness of the resulting execution is usually done informally. We develop a formal technique to analyze and reason about the correctness of the execution of an application at isolation levels other than SERIALIZABLE. We use a new notion of correctness, semantic correctness, a criterion weaker than serializability, to investigate correctness. In particular, for each isolation level, we prove a condition under which the execution of transactions at that level will be semantically correct. In addition to the ANSI/ISO isolation levels of READ UNCOMMITTED, READ COMMITTED, and REPEATABLE READ, we also prove a condition for correct execution at the READ-COMMITTED with first-committer-wins and at SNAPSHOT isolation. We assume that different transactions in the same application can be executing at different levels, but that each transaction is executing at least at READ UNCOMMITTED.

Many transaction processing applications execute at isolation levels lower than serializable in order to increase throughput and reduce response time. The problem is that non-serializable schedules are not guaranteed to be correct for all applications. The semantics of a particular application determines whether that application will run correctly at a lower isolation level, and in practice it appears that many applications do. Unfortunately, we know of an analysis technique that has been developed to test an application for its correctness at a particular level. Apparently decisions of this nature are made on an informal basis. In this paper we describe such a technique in a formal way. We use a new definition of correctness, semantic correctness, which is weaker than serializability, to investigate the correctness of such executions. For each isolation level, we prove a condition under which transactions that execute at that level will be semantically correct. In addition to the ANSI/ISO isolation levels of read uncommitted, read committed, and repeatable read, we also prove a condition for correct execution at the read committed with first-committer-wins (a variation of read committed) and at the snapshot isolation level. We assume that different transactions can be executing at different isolation levels, but that each transaction is executing at least at the read uncommitted level.