Metadata Issues in Long-term Management of Data and Metadata

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1 Issues in Long-term Management of Data and S. Sugimoto Faculty of Library, Information and Media Science, University of Tsukuba Japan C.Q. Li Graduate School of Library, Information and Media Studies, University of Tsukuba Japan ABSTRACT Without metadata, no data instances can be searched or accessed in the digital networked information environment. Longevity of data is a crucial issue for Data Science because we need to maintain data for the future users, which means that metadata the data must be maintained as well. This paper is aimed at presenting issues in the longevity of metadata in the networked information environment. It is broadly understood that metadata is an information science topic for describing and managing information resources. In the conventional information environment, longevity of metadata is realized by the maintenance of databases where metadata is stored. However, in the Linked Open Data environment of the Web, this conventional model would not work because of the nature of linked data, and we need to develop a new model to maintain metadata as a first class data object. This paper first mentions the background of metadata longevity and some basic concepts such as metadata schemas and meta-metadata. It discusses long-term maintenance of metadata in the context of Data Science. Then, it discusses some facets of metadata preservation and risks in maintenance of metadata, metadata provenance and aggregation in the context of long-term use of metadata. CCS CONCEPTS Information systems Storage management Theory of computation Data provenance KEYWORDS, metadata schema, provenance, data science, longterm maintenance ACM Reference format: S. Sugimoto and C.Q. Li In Information Science to Data Science: New Directions for ischools Workshop, Wuhan, Hubei, China, March 2017, 5 pages. 1 INTRODUCTION Data Science covers broad fields of data from scholarly data to governmental and industrial data, and from humanities and social sciences to natural sciences, technologies and medicine. Recent movements such as Open Data and Open Science have increased demands to develop knowledge, technologies and human resources in Data Science. Long-term and consistent management of data is a crucial issue in any fields where data curation and preservation are required. Long-term maintenance of data is not a very new research topic, but we need to understand the fundamental issues in the domain; because there are various genres of data, there are many different demands for the data longevity, and variety of communities responsible for keeping the data alive over time. This paper is intended to discuss the issues mainly from a viewpoint of metadata. This is because metadata is an essential component for data management in all fields and for various purposes. It is well known that metadata is a key topic for data science and that there are international standards of metadata for preservation of digital resources. On the other hand, the progress of our Internet-based information environment has brought up many new issues of metadata for data longevity. It is obvious that we need to consistently manage not only metadata, but also its schema, which is metadata metadata, i.e., meta-metadata. As metadata is a digital object we need to consistently maintain metadata schemas as a digital object in the networked information environment. has many facets, e.g., resource access, rights management, accessibility, preservation, and so forth. These metadata in different facets need to be maintained with the primary data in order to keep the data usable. Thus, a deep understanding the meta-level description is essential to keep data safe for the future in the networked information environment. This position paper is intended to raise metadata issues for data science education and research from the viewpoint of long-term maintenance of data. 2 METADATA IN DATA SCIENCE Data Science covers broad areas from humanities and social sciences to science, technology and medicine, and from scholarly data to governmental and industrial data. This means that specialists in data science may need two types of knowledge. One is knowledge data in a specific domain, e.g., physics, bioscience, humanities, social sciences, and so forth. The other is knowledge data neutral to any domains, which should cover different facets of data, e.g., data analysis, data organization, data curation, and so forth.

2 Information Science to Data Science: New Directions for ischools Workshop, Wuhan, Hubei PRC, March 2017 is a key component of the foundation knowledge of data because metadata is indispensable in various tasks in those facets. is one of the core subjects of Data Science, so that Data Science specialists need to understand not only metadata standards for particular data domains but also the foundations of metadata which include the underlying models of metadata, metadata technologies for the Web, and technologies and methodologies for interoperable metadata. Most of data stored in computers are data something, i.e., metadata 1. There are many metadata standards for describing resources from different viewpoints, e.g., general features, domain specific features, structure, technology, rights, provenance, and so forth. Those standards have their own underlying data models that reflect the purposes and domain specific features of the metadata [1]. in the networked information environment has features different from conventional metadata primarily designed for use on a single database or a set of databases. An instance of metadata on the Web is no longer an object enclosed in a database, but is an object which is transferred from a site to another and shared among those sites, which we call a first class object. schemas are metadata because they describe features of metadata, so that they may be transferred between computers as well if they are defined in a machine understandable form. Schemas which define metadata schemas are also metadata. Therefore, metadata schema is metadata metadata (i.e., meta-metadata), and schema to define metadata schemas is metadata metametadata (i.e., meta-meta-metadata). In theory, meta- may be endlessly repeated (Fig. 1). All of these meta-instances may be first class objects. This conceptual framework of meta-level description is a crucial aspect to define all kinds of data in a machine understandable form. Object Instance meta0: data Instance meta1: Figure 1: meta- relationships. Schema meta2: meta-metadata Meta- Schema meta3: meta-meta-metadata Interoperability of metadata is a very basic demand in the networked information environment where many different communities create and use metadata. There are many metadata standards defined for interoperability in an 1 Oxford dictionary defines data as Facts and statistics collected together for reference or analysis. ( In addition to this definition, data is used in technology in the meaning Numbers, characters, images, or other method ofrecording, in a form which can be assessed by a human or (especially) input into a computer, stored and processed there, or transmitted on some digital channel. ( S. Sugimoto and C.Q. Li application domain and its community. Dublin Core is a wellknown metadata standard for semantic metadata interoperability across communities. Resource Description Framework (RDF) is a W3C standard which defines model, syntax and vocabulary description scheme for metadata exchangeable on the Web. The basic concepts developed along with the development of these Web-oriented standards are primarily for metadata interoperability across domains. On the other hand, metadata interoperability over time is not well developed yet, despite it being a crucial issue for the longevity of valuable data. 3 LONG-TERM MAINTENANCE OF DATA AND METADATA There is no question the importance of long-term maintenance of data whose values are well recognized. Data is stored as a dataset in various environments, i.e., relational databases, spreadsheet files, document files, and so forth. From our experiences we know that those datasets created in small research projects tend to be poorly maintained and easily lost when the projects are finished. Even if the datasets are retained safe as a collection of bits, they become meaningless if their schemas are lost. Therefore, long-term maintenance of a dataset has two facets maintenance of the body of the dataset, and maintenance of metadata of the dataset. There are well-known standards for the former such as OAIS and PREMIS but the latter is still a new topic. Those standards are primarily for preserving digital objects. They may be applied to metadata preservation because metadata is a digital object. However, metadata has to be understandable not only by humans but also machines. For example, OAIS defines Preservation Description Information (PDI) as metadata an Information Object and enclosed with the object in an Information Package (IP). PDI need to be correctly interpretable not only by an OAIS-based preservation system but also by systems which receive IPs. schemas must be maintained in order to keep metadata machine-processable over time. schemas are preserved as a document for human readers in a conventional maintenance environment of metadata. However, the state of the art environment of the Web provides standards to formally describe metadata schemas in a machine-processable form, e.g., RDF and OWL. The Dublin Core Application Profiles framework, which provides a basic framework of metadata schemas for interoperable metadata, is defined based on the Web standards. Europeana Data Model (EDM) is defined using RDF. Regarding metadata vocabularies, many metadata element sets and controlled vocabularies such as LCSH and Getty Thesauri are described in RDF and made available on the Web. Thus, many metadata schemas are already provided in machine-processable forms. 2

3 Issues in Long-term Management of Data and This transition of metadata schema description from semi-formal description in a natural language to formal machine-processable language will significantly affect maintenance of data. In the state-of-the-art Web environment, metadata and metadata schemas are no longer simple document-like objects, but are complex objects transferrable over networks. The authors consider that we need to understand metadata issues in the long-term maintenance of data from the viewpoint of metadata as first class object. 4 MAINTENANCE OF METADATA FACETS AND RISKS This paper is intended to discuss issues crucial for the longevity of metadata. In the previous work by the authors [2], we presented the issues for permanence of metadata and discussed facets of metadata schemas and risks in longevity of metadata and their schemas based on the facets. Risk management against loss or damage of data is not a simple task. We need to understand features of data to find the risks. Those features are expressed as metadata, so that we need to understand features of metadata for managing risks. The following sections discusses facets of metadata from the viewpoint of long-term maintenance and risks in the longterm maintenance of metadata. 4.1 Facets for Long-Term Maintenance of Schemas maintenance is to keep metadata machineprocessable as intended. Therefore, we need to maintain metadata schemas that define both semantic and syntactic features of metadata. The facets shown below are defined from the viewpoint of type of metadata instances. (1) Entity Format Types Document Files, Databases, XML Encoded Text: Longevity management of metadata entities depends on the implementation formats of entities to be preserved, e.g., PDF, SQL, RDF/XML, etc. (2) Entity Types Meta-Levels: By definition, metadata is data data, which we can rephrase as a description something. is description an object, metadata schema is description a metadata instance, and metadata schema definition language is metadata metadata schema. Thus, we can define levels of description as meta-levels, which help us identify entities to be preserved. (3) Schema Components: The Singapore Framework of Dublin Core Application Profiles clearly gives abstract components of metadata schemas. It gives us hints to identify components of metadata schemas and those of meta-schemas. (4) Dynamic Entities: We often create entities dynamically in accordance with requirements in applications. An example is a mapping table between different metadata schemas to make the metadata in those schemas interoperable. We need to record the mapping table as Information Science to Data Science: New Directions for ischools Workshop, March 2017, Wuhan, Hubei PRC well as those metadata schemas. (5) Documentation: Documents metadata are very basic objects that should be maintained for the future. 4.2 Risks in Long-term Maintenance of As discussed previously, there are meta-instances of data, e.g., metadata schema as meta-metadata, schema describing metadata schema as meta-meta-metadata, and so forth. These meta-instances are also data, which may be transferred and shared in the Web-based information environment. We need to know what kind of risks exist in order to develop technologies to keep those data instances consistently processable over time. Below are several typical risks in the long-term maintenance of metadata. (1) Unknown metadata schema: schema of this metadata instance is unknown. (2) Poorly maintained metadata schema documents: We cannot display a metadata creation guideline documents correctly. (3) Obsolete metadata vocabulary standards: An application uses a standardized metadata vocabulary but the name of the vocabulary is unknown. (4) Obsolete identifiers and identification schemes: Definition of a metadata term is not identifiable by URI given to the term. (5) Improperly recorded revision history: There are sets of metadata which are created based on different versions of a metadata schema but change logs are not recorded. There are many other types of risks. Those risks cause difficulties in proper interpretation of metadata by both machines and humans. Preservation of digital objects is to keep the objects safe from various types of risks of damage and loss. Preservation policy depends on the objects as well as the stakeholders for preservation and preservation technologies available. Preservation of a digital object is not same as maintaining the object in a useable state. Preservation of a digital object is to keep its content usable for the future. A fundamental difference between digital and non-digital object is that we can copy and transfer contents of digital object but we cannot do the same for non-digital objects. Preservation of digital objects is to preserve information as a bit-sequence. and their schemas are a typical digital object because the physical media in which they are stored does not have meanings in understanding the metadata and their schemas. We need to detect risks of loss or damage in the preservation process of any digital objects. and their schemas in the LOD environment are a digital object as discussed in the previous section. Risk management for keeping metadata and their schemas safe is a crucial research area. For example, metadata registries have crucial roles for risk management. There are registries for metadata registries: 3

4 Information Science to Data Science: New Directions for ischools Workshop, Wuhan, Hubei PRC, March 2017 DCMI Registry 2 and Open Registry 3 provide metadata vocabularies; PRONOM 4 provides file formats. Those registries provide information metadata vocabularies and file formats with chronological information, which is crucial to detect risks. Risk Management in the Linked Open Data environment is a very new and important area but has not been studied well yet. 5 PROVENANCE DESCRIPTION OF METADATA SCHEMAS Preservation Description Information (PDI) defined in Open Archival Information System (OAIS) defines five criteria of information required for preservation of digital objects [3]. OAIS is basically designed to store a digital object in a package, so that it does not well suit to preservation of digital objects which are kept available on the Internet. However, PDI gives us important facets of digital objects from the viewpoint of preservation. The criteria given in PDI are Provenance, Reference, Fixity, Context and Access Rights Information. Information in all these criteria is crucial to keep data processable over time. Among these criteria, we focus on Provenance because provenance information which is a chain of changes recorded chronologically is essential to trace changes and find risks of data inconsistency [4]. The authors have developed a simple provenance model for metadata schemas based on the Dublin Core Application Profiles and the PROV standard defined by the World-Wide Web Consortium (W3C) [5-8]. Our model is based on the following framework. (1) Define provenance description schemes for metadata vocabularies and Description Set Profiles (DSPs). (2) Define simple provenance description schemes. (3) Use RDF as the base data model and encoding syntax of provenance description The model defines Activities and Entities of PROV. The main Activities are generalized into three change types Addition, Deletion and Revision. The main Entities are metadata terms and vocabularies, and DSPs and its components. The detailed explanation of the model is not included in this paper as this paper is not intended to describe the details of the model. 6 METADATA AGGREGATION There are many databases whose contents are metadata. On one hand, those databases are important resource for data analysis, e.g., big data analysis. On the other hand, they are used as source information to add values to the existing information. Federated search and metadata aggregation based on harvesting are typical value-added services. Open 2 DCMI Registry, 3 Open Registry, 4 The Technical Registry PRONOM, S. Sugimoto and C.Q. Li Archives Initiative (OAI) defined a model to aggregate metadata [9]. Europeana aggregates metadata harvested from participating institution to create a large-scale portal to European cultural heritage [10]. aggregation is a crucial technology to develop an integrated dataset from existing heterogeneous datasets. The global trend of Linked Open Data pushes aggregating heterogeneous information resources. For example, we can link a bibliographic record of a novel to an article the novel in Wikipedia, and to a movie created from the novel. This linkage is an aggregation of metadata, and an expression of the linkage is a metadata the aggregation. In the previous works at the authors laboratory, we have studied metadata aggregation for cultural heritage and popculture resources [11,12]. We have learned LOD provides us with basic technologies for metadata aggregation but we need to find semantic commonality in the resources to be linked, i.e., semantic aggregation. Semantic aggregation of metadata is still challenging but it is a crucial technology to add values to existing datasets in the LOD environment. In addition, it is important to record aggregation information as a metadata and keep it for the future use. 7 CONCLUSIONS The goal of this paper is to address issues in longevity of data from the viewpoint of metadata. A large portion of data available on the Web is metadata, and the longevity of data is obviously a crucial issue. plays important roles for the longevity of data. is data, so that metadata metadata has important roles for the longevity of metadata. Thus, metadata schemas must be maintained to keep the metadata understandable over time. In our conventional environment, metadata schemas are presented as a document or set of documents. However, in the LOD environment, metadata schemas can be realized as a set of digital objects using the same scheme to express metadata instances, i.e., RDF. Those metadata schema objects must be kept machine understandable as well as the metadata instances. This means that scheme to define metadata schemas must be maintained as well. Maintaining primary data, metadata, meta-metadata, metameta-metadata and so forth in the networked information environment is still a new research area of Data Science where knowledge, technologies and experiences in Information Science have great values. A HEADINGS IN APPENDICES A.1 Introduction A.2 in Data Science A.3 Long-term Maintenance of Data and 4

5 Issues in Long-term Management of Data and Information Science to Data Science: New Directions for ischools Workshop, March 2017, Wuhan, Hubei PRC A.4 Maintenance of Facets and Risks A.4.1 Facets for Long-term Maintenance of Schemas A.4.2 Risks in Long-term Maintenance of A.5 Provenance Description of Schemas A.6 Aggregation A.7 Conclusions A.8 References ACKNOWLEDGMENTS This work was partially supported by JSPS Kaken Grant-in-Aid for Scientific Research (A) (Grant No.: 16H01754). REFERENCES [1] J. Greenberg Understanding metadata and metadata schemes. Cataloging & classification quarterly, 40(3-4), DOI: [2] S. Sugimoto, C.Q. Li, M. Nagamori, and J. Greenberg Permanence and Temporal Interoperability of in the Linked Open Data Environment. In Proceeding of the International Conference on Dublin Core and Applications 2016 (DC-2016). Copenhagen, Denmark, 45-54, [3] Consultative Committee for Space Data Systems. Reference Model for an Open Archival Information System (OAIS), Magenta Book (CCSDS M- 2), [4] K. Eckert Provenance and Annotations for Linked Data. In Proceeding of the International Conference on Dublin Core and Applications 2013 (DC-2013), Lisbon, Portugal, 9-18, [5] M. Nilsson, T. Baker, and P. Johnston. The Singapore Framework for Dublin Core Application Profiles, 2008, [6] C.Q. Li, M. Nagamori, and S. Sugimoto Temporal Interoperability of : An Interoperability-based View for Longevity of. In Proceeding of the 6 th International Conference on Asia-Pacific Library and Information Education and Practice (ALIEP 2015). Manila, Philippines, [7] C.Q. Li and S. Sugimoto Provenance Description of Vocabularies for the Long-term Maintenance of. In Proceeding of the 7 th International Conference on Asia-Pacific Library and Information Education and Practice (ALIEP 2016). Nanjing, China, [8] Y. Gil and S. Miles (Eds) PROV Model Primer, [9] Open Archives Initiative. Object Exchange and Reuse (OAI-ORE), [10] A. Isaac (Ed.) Europeana Data Model Primer, echnical_requirements/edm_documentation/edm_primer_ pdf [11] S. Kiryakos, S. Sugimoto, M. Nagamori, and T. Mihara Aggregating from Heterogeneous Pop-Culture Resources on the Web. In Proceeding of the International Conference on Dublin Core and Applications 2016 (DC-2016). Copenhagen, Denmark, 65-74, [12] C. Wijesundara, S. Sugimoto, B. Narayan, and K. Tuamsuk An Exploratory Aggregation Model for Sri Lankan Heritage and its Extension: Bringing Cultural Heritage Information from Developing Regions to the Global Information Space as Linked Open Data. In Proceeding of the 7 th International Conference on Asia-Pacific Library and Information Education and Practice (ALIEP 2016). Nanjing, China,