Software Citation Implementation Challenges

The main output of the FORCE11 Software Citation working group (https://www.force11.org/group/software-citation-working-group) was a paper on software citation principles (https://doi.org/10.7717/peerj-cs.86) published in September 2016. This paper laid out a set of six high-level principles for software citation (importance, credit and attribution, unique identification, persistence, accessibility, and specificity) and discussed how they could be used to implement software citation in the scholarly community. In a series of talks and other activities, we have promoted software citation using these increasingly accepted principles. At the time the initial paper was published, we also provided guidance and examples on how to make software citable, though we now realize there are unresolved problems with that guidance. The purpose of this document is to provide an explanation of current issues impacting scholarly attribution of research software, organize updated implementation guidance, and identify where best practices and solutions are still needed

Long-term Information Preservation and Access

An unprecedented amount of information encompassing almost every facet of human activities across the world is generated daily in the form of zeros and ones, and that is often the only form in which such information is recorded. A good fraction of this information needs to be preserved for periods of time ranging from a few years to centuries. Consequently, the problem of preserving digital information over a long-term has attracted the attention of many organizations, including libraries, government agencies, scientific communities, and individual researchers. In this dissertation, we address three issues that are critical to ensure long-term information preservation and access. The first concerns the core requirement of how to guarantee the integrity of preserved contents. Digital information is in general very fragile because of the many ways errors can be introduced, such as errors introduced because of hardware and media degradation, hardware and software malfunction, operational errors, security breaches, and malicious alterations. To address this problem, we develop a new approach based on efficient and rigorous cryptographic techniques, which will guarantee the integrity of preserved contents with extremely high probability even in the presence of malicious attacks. Our prototype implementation of this approach has been deployed and actively used in the past years in several organizations, including the San Diego Super Computer Center, the Chronopolis Consortium, North Carolina State University, and more recently the Government Printing Office. Second, we consider another crucial component in any preservation system - searching and locating information. The ever-growing size of a long-term archive and the temporality of each preserved item introduce a new set of challenges to providing a fast retrieval of content based on a temporal query. The widely-used cataloguing scheme has serious scalability problems. The standard full-text search approach has serious limitations since it does not deal appropriately with the temporal dimension, and, in particular, is incapable of performing relevancy scoring according to the temporal context. To address these problems, we introduce two types of indexing schemes - a location indexing scheme, and a full-text search indexing scheme. Our location indexing scheme provides optimal operations for inserting and locating a specific version of a preserved item given an item ID and a time point, and our full-text search indexing scheme efficiently handles the scalability problem, supporting relevancy scoring within the temporal context at the same time. Finally, we address the problem of organizing inter-related data, so that future accesses and data exploration can be quickly performed. We, in particular, consider web contents, where we combine a link-analysis scheme with a graph partitioning scheme to put together more closely related contents in the same standard web archive container. We conduct experiments that simulate random browsing of preserved contents, and show that our data organization scheme greatly minimizes the number of containers needed to be accessed for a random browsing session. Our schemes have been tested against real-world data of significant scale, and validated through extensive empirical evaluations

A Configuration Management System for Software Product Lines

Software product line engineering (SPLE) is a methodology for developing a family of software products in a particular domain by systematic reuse of shared code in order to improve product quality and reduce development time and cost. Currently, there are no software configuration management (SCM) tools that support software product line evolution. Conventional SCM tools are designed to support single product development. The use of conventional SCM tools forces developers to treat a software product line as a single software project by introducing new programming language constructs or using conditional compilation. We propose a research conguration management prototype called Molhado SPL that is designed specifically to support the evolution of software product lines. Molhado SPL addresses the evolution problem at the configuration level instead of at the code level. We studied the type of operations needed to support the evolution of software product lines and proposed a versioning model and eight cases of change propagation. Molhado SPL supports independent evolution of core assets and products, the sharing of code and the tracking relationships between products and shared code, and the eight cases of change propagation. The Molhado SPL consists of four layers with each layer providing a different type of service. At the heart of Molhado SPL are the versioning model, component object, shared component object, and project objects that allow for independent evolution of products and shared artifacts, for sharing, and for supporting change propagation. Furthermore,they allow product specific changes to shared code without interfering with the core asset that is shared. Products can also introduce product specific assets that only exist in that product. In order to for Molhado SPL to support product line, we implemented XML merging, feature model editing and debugging, and version-aware XML documents. To support merging of XML documents, we implemented a 3-way XML document merging algorithm that uses versioned data structures, change detection, and node identity. To support software product line derivation or modeling of software product line, we implemented support for feature model including editing and debugging. Finally, we created the version-aware XML document framework to support collaborative editing of XML documents without requiring a version repository. The version history is embedded in the documents using XML namespaces, so that the documents remain valid under the XML specification. The version-aware XML framework can also be used to support the exporting of documents from Molhado SPL repository to be edit outside and import back the change history made to the document. We evaluated Molhado SPL with two product lines: a document product line and a the graph data structures product line. This evaluation showed that Molhado SPL supports independently evolution of products and core assets and the eight change propagation cases. We did not evaluate MolhadoSPL in terms of scalability or usability. The main contributions of this dissertation research are: 1) Molhado SPL that supports the evolution of product lines, 2) a fast 3-way XML merge algorithm, 3) a version-aware XML document framework, and 4) a feature model editor and debugger

Design and development of a generalized LIDAR point cloud streaming framework over the web

LIDAR data, retrieved by laser systems carried by airplanes, helicopters or cars, require the storage of millions of point information concerning the space we are scanning. This work illustrates the implementation of a web application which gets LIDAR data from a PostreSQL database and creates n different levels of detail of the point cloud data and saves them into related files. A first operation implemented on the data is linear point to point measurementope

Attribute-Level Versioning: A Relational Mechanism for Version Storage and Retrieval

Data analysts today have at their disposal a seemingly endless supply of data and repositories hence, datasets from which to draw. New datasets become available daily thus making the choice of which dataset to use difficult. Furthermore, traditional data analysis has been conducted using structured data repositories such as relational database management systems (RDBMS). These systems, by their nature and design, prohibit duplication for indexed collections forcing analysts to choose one value for each of the available attributes for an item in the collection. Often analysts discover two or more datasets with information about the same entity. When combining this data and transforming it into a form that is usable in an RDBMS, analysts are forced to deconflict the collisions and choose a single value for each duplicated attribute containing differing values. This deconfliction is the source of a considerable amount of guesswork and speculation on the part of the analyst in the absence of professional intuition. One must consider what is lost by discarding those alternative values. Are there relationships between the conflicting datasets that have meaning? Is each dataset presenting a different and valid view of the entity or are the alternate values erroneous? If so, which values are erroneous? Is there a historical significance of the variances? The analysis of modern datasets requires the use of specialized algorithms and storage and retrieval mechanisms to identify, deconflict, and assimilate variances of attributes for each entity encountered. These variances, or versions of attribute values, contribute meaning to the evolution and analysis of the entity and its relationship to other entities. A new, distinct storage and retrieval mechanism will enable analysts to efficiently store, analyze, and retrieve the attribute versions without unnecessary complexity or additional alterations of the original or derived dataset schemas. This paper presents technologies and innovations that assist data analysts in discovering meaning within their data and preserving all of the original data for every entity in the RDBMS