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Designing metadata workflows is a core competency for cataloging and metadata practitioners. Unlike many other competencies, metadata workflow design lacks a conceptual framework, hindering understanding and skill development. This article presents a framework for metadata workflows that describes processes for adding, enhancing, and removing metadata from library systems. The framework describes standard and custom workflows, including original and copy cataloging, withdrawals, system migrations, metadata creation, and record enhancement. A better understanding of metadata workflows facilitates the implementation of automation and batch processing and can inform staff training, professional development offerings, and library and information science curricula. [ABSTRACT FROM AUTHOR]

The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called Theme (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back‐tracking; and fine‐grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data. [ABSTRACT FROM AUTHOR]

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times. [ABSTRACT FROM AUTHOR]

Data lakes are storage repositories that contain large amounts of data in its native format; either structured ssemi-structured or unstructured, to be used when needed. Data lakes are open to a wide range of use cases such as carrying out advanced analytics, extracting knowledge patterns, etc. However, simply dumping all the data into a data lake would only lead to a so-called data swamp. To prevent such a situation, enterprises can adopt best practices among which to build and maintain metadata. In recent years there has been a growing body of research about managing metadata in data lake environments. Existing research efforts deal separately with different activities such as metadata modeling, metadata capture and extraction, metadata usage, etc. Nevertheless, despite its importance, a global view about the research landscape about metadata management for data lakes is still missing. This survey congregates different facets of metadata management in data lakes and presents a global view along with the technological implications and the required features for building successful metadata management systems. Besides, this survey summarizes and discusses research gaps, open problems and main challenges facing both industrialists and academics. This survey pertains to the broader field of Big Data and especially to the data platforms that manage enterprise big data assets. Furthermore, considering the parallels between data lakes and digital libraries regarding their dependence on metadata for content management, this study could offer valuable insights to the digital library community, offering them a technological outlook on metadata management. [ABSTRACT FROM AUTHOR]

This study examines the integration of artificial intelligence (AI) into metadata management in libraries, focusing on its challenges, opportunities, and future trends. Metadata plays a critical role in organizing and retrieving library resources, and AI offers the potential to automate these processes. The objectives of this study are to explore the challenges libraries face with AI-driven metadata, identify opportunities for improvement, and analyze emerging trends. Using a systematic literature review, relevant articles were analyzed to draw insights. Key findings reveal that data quality, ethical concerns, and technical limitations are major challenges. However, AI presents opportunities such as automating metadata creation, improving resource discoverability, and efficiently managing large digital collections. Future trends include the increased use of linked data and predictive analytics, which will shape how metadata is managed. The study concludes that while AI brings significant benefits, human oversight is essential, and libraries need to focus on training staff and updating policies for effective AI integration. [ABSTRACT FROM AUTHOR]

Data lakes (DL) provide powerful capabilities for processing and utilizing large and diverse data, helping organizations adapt to the modern environment and extract maximum value from the information at their disposal. Effective data analysis provides actionable knowledge which is a competitive advantage for organizations. Metadata management in data lakes is a key element in ensuring their full functionality. At the same time, this is a dynamic and under-researched area that reflects the rapid development of information technology and the business needs for effective data management. The research is based on a thorough scientific analysis of existing publications on the chosen topic. For this purpose, up-to-date and relevant open access publications from Scopus and Web of Science that correspond to the keywords "data lake" and "metadata" are identified and are from the last 15 years. Based on a review of the existing literature, the main challenges in data lake metadata management are highlighted. The goal of the research is to summarize the existing models in the field of metadata management in data lakes and to propose a new conceptual framework that can serve as a useful guide for designing and implementing metadata management models in heterogeneous data warehouses, as well as implementation steps. The concept's adoption involves a detailed study of the data management model in a specific organization, a measurement of the level of effectiveness after the model's implementation, and the use of additional metrics to confirm its feasibility. These tasks are therefore the subject of future research. Another limitation of the proposed framework is that it does not address in depth the rules and standards related to ensuring data security, which would be of the highest priority especially in sectors such as finance, defence and healthcare. In addition, further research could also focus on future analysis of the level of satisfaction with the transformation of metadata management processes. [ABSTRACT FROM AUTHOR]

Analytical data platforms have been used for decades to improve organizational performance. Starting from the data warehouses used primarily for structured data processing, through the data lakes oriented for raw data storage and post-hoc data analyses, to the data lakehouses—a combination of raw storage and business intelligence pre-processing for improving the platform's efficacy. But in recent years, a new architecture called Data Mesh has emerged. The main promise of this architecture is to remove the barriers between operational and analytical teams in order to boost the overall value extraction from the big data. A number of attempts have been made to formalize and implement it in existing projects. Although being defined as a socio-technical paradigm, data mesh still lacks the technology support to enable its widespread adoption. To overcome this limitation, we propose a new view of the platform requirements alongside the formal governance definition that we believe can help in the successful adoption of the data mesh. It is based on fundamental aspects such as decentralized data domains and federated computational governance. In addition, we also present a blockchain-based implementation of a mesh platform as a practical validation of our theoretical proposal. Overall, this article demonstrates a novel research direction for information system decentralization technologies. [ABSTRACT FROM AUTHOR]

Abstract In this big data era, multitudes of data are generated and collected which contain the potential to gain new insights, e.g., for enhancing business models. To leverage this potential through, e.g., data science and analytics projects, the data must be made available. In this context, data marketplaces are used as platforms to facilitate the exchange and thus, the provisioning of data and data-related services. Data marketplaces are mainly studied for the exchange of data between organizations, i.e., as external data marketplaces. Yet, the data collected within a company also has the potential to provide valuable insights for this same company, for instance to optimize business processes. Studies indicate, however, that a significant amount of data within companies remains unused. In this sense, it is proposed to employ an Enterprise Data Marketplace, a platform to democratize data within a company among its employees. Specifics of the Enterprise Data Marketplace, how it can be implemented or how it makes data available throughout a variety of systems like data lakes has not been investigated in literature so far. Therefore, we present the characteristics and requirements of this kind of marketplace. We also distinguish it from other tools like data catalogs, provide a platform architecture and highlight how it integrates with the company’s system landscape. The presented concepts are demonstrated through an Enterprise Data Marketplace prototype and an experiment reveals that this marketplace significantly improves the data consumer workflows in terms of efficiency and complexity. This paper is based on several interdisciplinary works combining comprehensive research with practical experience from an industrial perspective. We therefore present the Enterprise Data Marketplace as a distinct marketplace type and provide the basis for establishing it within a company.

The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and superior sequential performance provided by non-volatile memories (NVMs). This article proposes FlatFS+, an NVM file system that features a flat namespace architecture while providing a compatible hierarchical namespace view. FlatFS+ incorporates three novel techniques: the direct file path walk model, range-optimized Br tree, and compressed index key design with scan and write dual optimization, to fully exploit flat namespace to improve file system metadata performance on ultra-fast, byte-addressable NVMs. Evaluation results demonstrate that FlatFS+ achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems. [ABSTRACT FROM AUTHOR]

Secondary use of clinical health data implies a prior integration of mostly heterogenous and multidimensional data sets. A clinical data warehouse addresses the technological and organizational framework conditions required for this, by making any data available for analysis. However, users of a data warehouse often do not have a comprehensive overview of all available data and only know about their own data in their own systems - a situation which is also referred to as 'data siloed state'. This problem can be addressed and ultimately solved by implementation of a data catalog. Its core function is a search engine, which allows for searching the metadata collected from different data sources and thereby accessing all data there is. With this in mind, we conducted an explorative online market survey followed by vendor comparison as a pre-requisite for system selection of a data catalog. Assessment of vendor performance was based on seven predetermined and weighted selection criteria. Although three vendors achieved the highest score, results were lying closely together. Detailed investigations and test installations are needed for further narrowing down the selection process. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional data storage technologies were no longer suitable for data analysis and application in the era of big data. The emergence of the concept of Data Lake effectively solved the problems of high data storage costs, low flexibility, and heterogeneous data diversification. The research on Data Lake was still in its early stage, and there was a lack of comprehensive research and discussion covering the entire process of data processing. In order to help followup researchers understand Data lake technology more comprehensively, this paper reviewed the research results of Data lake technology in recent years. Firstly, it sorted out the development history and concept of Data Lake, and compared with other similar concepts. Secondly, it investigated the Data Lake architecture, and the key technologies of the data lake divided into storage, data ingestion, data maintenance, data exploration, and data governance according to the architecture of characteristics. It analyzed and discussed the latest research progress, technical solutions, research deficiencies, and future research directions of key technologies. Finally, it investigated the typical applications of data lakes in various application fields, providing references for implementers of data lakes in various industries. [ABSTRACT FROM AUTHOR]

In this big data era, multitudes of data are generated and collected which contain the potential to gain new insights, e.g., for enhancing business models. To leverage this potential through, e.g., data science and analytics projects, the data must be made available. In this context, data marketplaces are used as platforms to facilitate the exchange and thus, the provisioning of data and data-related services. Data marketplaces are mainly studied for the exchange of data between organizations, i.e., as external data marketplaces. Yet, the data collected within a company also has the potential to provide valuable insights for this same company, for instance to optimize business processes. Studies indicate, however, that a significant amount of data within companies remains unused. In this sense, it is proposed to employ an Enterprise Data Marketplace, a platform to democratize data within a company among its employees. Specifics of the Enterprise Data Marketplace, how it can be implemented or how it makes data available throughout a variety of systems like data lakes has not been investigated in literature so far. Therefore, we present the characteristics and requirements of this kind of marketplace. We also distinguish it from other tools like data catalogs, provide a platform architecture and highlight how it integrates with the company's system landscape. The presented concepts are demonstrated through an Enterprise Data Marketplace prototype and an experiment reveals that this marketplace significantly improves the data consumer workflows in terms of efficiency and complexity. This paper is based on several interdisciplinary works combining comprehensive research with practical experience from an industrial perspective. We therefore present the Enterprise Data Marketplace as a distinct marketplace type and provide the basis for establishing it within a company. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications. [ABSTRACT FROM AUTHOR]

Maschinelle Verfahren als Instrumente, um die Open-Access-Transformation in Form von Publikationsdaten abzubilden, erfordern aufgrund enormer Datenvolumen strukturierte Datensätze, die eindeutig identifizierbar sind und sich vergleichen lassen. Zur Identifikation wissenschaftlicher und praxisorientierter Publikationen haben sich digital object identifier (DOI) durchgesetzt, doch längst nicht alle Publikationen sind damit ausgestattet. Anhand eines Datensamples der Pädagogischen Hochschule Zürich werden in dieser Studie ausgewählte Datenquellen für Monitoring-Verfahren analysiert, um Quoten für nicht-repräsentierte Publikationen zu berechnen und unbeabsichtigte Leerstellen sichtbar zu machen. Das Verstehen dieser Leerstellen eröffnet Lösungsansätze, wie diese vermieden werden können, um ein belastbares Open-Access-Monitoring sicherzustellen. Due to the enormous data volumes, monitoring methods as tools for representing the Open Access transformation in terms of publications require structured data sets that can be uniquely identified and compared. Digital object identifiers (DOI) have been established for the identification of scientific and practice-oriented publications, but certainly not all publications are endowed with them. Using a reasonable data sample from the University of Teacher Education Zurich, this study analyzes selected data sources for monitoring routines to calculate quotas for non-represented publications and to make unintentional empty spaces visible. This understanding of empty spaces opens approaches on how to avoid them to ensure resilient open access monitoring. [ABSTRACT FROM AUTHOR]

Data catalogs represent a promising solution for semantically classifying and organizing data sources and enriching raw data with metadata. However, recent research has shown that data catalogs are difficult to implement due to the complexity of the data landscape or issues with data governance. Moreover, data catalogs struggle to enable business analysts to find the data they need for their use cases. Against this backdrop, we develop a self-service system that automatically extracts metadata from a data lake and enables business analysts to explore the metadata through an easy-to-use interface. Specifically, instead of implementing the data catalog top-down, our system derives metadata from user queries bottom-up. Hereby, we conduct 15 interviews with business analysts to derive the underlying requirements of the system and evaluate its features with a focus group. Our findings illustrate that participants especially value the possibility to reuse queries from other users and appreciated the support in query validation as data preparation is a complex and time-consuming endeavour. [ABSTRACT FROM AUTHOR]

Despite investing heavily in data-related technology and human resources, enterprises are still struggling to derive value from data. To foster data value creation and move toward a data-driven enterprise, adequate data management and data governance practices are fundamental. To support these practices, organizations are building (meta)data management landscapes by combining different tools. Data catalogs are a central part of these landscapes as they enable an overview of available data assets and their characteristics. To deliver their highest value, data catalogs need to be integrated with existing data sources and other data management tools. However, enterprises struggle with data catalog integration because (a) not all data catalog application types foster enterprise-wide data management and data governance alike, and (b) several technical characteristics of data catalog integration remain unclear. These include the supported data sources, data catalog federation, and ways to provision data access. To tackle these challenges, this paper first develops a typology of data catalog applications in the enterprise context. Based on a review of the academic literature and an analysis of data catalog offerings, it identifies four enterprise-internal and three cross-enterprise classes of data catalog applications. Second, an in-depth analysis of 51 data catalog offerings that foster enterprise-wide metadata management examines key characteristics of the technical integration of data catalogs. [ABSTRACT FROM AUTHOR]

The particularities of the agricultural ecosystem of cooperating partners suggest a highly distributed, open data architecture to support the exchange and re-use of data and data-driven applications. We describe the data strategy and the federated basic architecture of the Agri-Gaia ecosystem. We present an ontology-based approach to metadata management which extends a bucket store for arbitrary data storage by an RDF-based metadata graph store and employs widely-used domain ontologies as a conceptual basis. Data and service providers are free to extend the describing metadata at any time, according to their needs. The resulting set of interconnected platforms supports the publication, retrieval and secure sharing of exposable data under full control of their owners. [ABSTRACT FROM AUTHOR]

Analytical data platforms have been used for decades to improve organizational performance. Starting from the data warehouses used primarily for structured data processing, through the data lakes oriented for raw data storage and post-hoc data analyses, to the data lakehouses—a combination of raw storage and business intelligence pre-processing for improving the platform’s efficacy. But in recent years, a new architecture called Data Mesh has emerged. The main promise of this architecture is to remove the barriers between operational and analytical teams in order to boost the overall value extraction from the big data. A number of attempts have been made to formalize and implement it in existing projects. Although being defined as a socio-technical paradigm, data mesh still lacks the technology support to enable its widespread adoption. To overcome this limitation, we propose a new view of the platform requirements alongside the formal governance definition that we believe can help in the successful adoption of the data mesh. It is based on fundamental aspects such as decentralized data domains and federated computational governance. In addition, we also present a blockchain-based implementation of a mesh platform as a practical validation of our theoretical proposal. Overall, this article demonstrates a novel research direction for information system decentralization technologies.

Abstract The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific articles. Here, we harness state-of-the-art machine learning techniques and develop a smart neuroscience metadata suggestion system accessible by both humans through a user-friendly graphical interface and machines via Application Programming Interface. We demonstrate a practical application to the public repository of neural reconstructions, NeuroMorpho.Org, thus expanding the existing web-based metadata management system currently in use. Quantitative analysis indicates that the suggestion system reduces personnel labor by at least 50%. Moreover, our results show that larger training datasets with the same software architecture are unlikely to further improve performance without ad-hoc heuristics due to intrinsic ambiguities in neuroscience nomenclature. All components of this project are released open source for community enhancement and extensions to additional applications.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: In the eScience environment, digital research objects are characterized by having a complex and long-life cycle, which depends on different disciplinary contexts and perspectives of (re)use. This lifecycle starts before the start of research and extends beyond the end of the project, along this journey, various types of metadata are added to objects, assigned by different actors, including those generated automatically by scientific instruments and workflow tools, in a continuous process of adding value to datasets and other research objects. In this context, digital research objects are accompanied by a wide range of metadata - with many functions and properties - that often surpass the data themselves in volume and even in importance, configuring a "scientific big metadata" that is difficult to organize and manage. Objective: Systematically present the functions of new metadata to support metadata management and the construction of disciplinary schemes. Methodology: Underlying the construction of the proposal, four axes provide methodological support to the study: historical, pragmatic, standardization, and epistemological. Results: As a result, a model is proposed for schematizing the various elements of metadata based on their functionalities, based on the assumption of the connection between eScience and Information Science established by big metadata. Conclusion: It is concluded that big metadata creates a connection between eScience and CI, and that in addition to the need to curate research objects, specific FAIR management of metadata is also necessary. [ABSTRACT FROM AUTHOR]

The author concentrates on data governance in research and practice. The author has developed a strong knowledge of data governance through numerous case studies of electricity, retail, telecom, banks, or insurance companies. With more than nine years of experience from both technical and process point of view, the author has become a Data Governance specialist with focus on design and implementation of governance frameworks. The author has given numerous training courses to improve knowledge of the data to derive maximum value from it. Data governance in financial institutions has been significantly driven by regulatory requirements. Data governance standards are complex and lack implementation details. This paper proposes Data Governance Model used in a financial institution. Data Governance Model is based on BCBS 239 and DAMA methodology. The Model enhances practices of DAMA and tailors them to the needs of financial institution. The scope of the Data Governance Model is limited to data governance, data quality, and metadata management. [ABSTRACT FROM AUTHOR]

When using distributed storage systems to store gridded remote sensing data in large, distributed clusters, most solutions utilize big table index storage strategies. However, in practice, the performance of big table index storage strategies degrades as scenarios become more complex, and the reasons for this phenomenon are analyzed in this paper. To improve the read and write performance of distributed gridded data storage, this paper proposes a storage strategy based on Ceph software. The strategy encapsulates remote sensing images in the form of objects through a metadata management strategy to achieve the spatiotemporal retrieval of gridded data, finding the cluster location of gridded data through hash-like calculations. The method can effectively achieve spatial operation support in the clustered database and at the same time enable fast random read and write of the gridded data. Random write and spatial query experiments proved the feasibility, effectiveness, and stability of this strategy. The experiments prove that the method has higher stability than, and that the average query time is 38% lower than that for, the large table index storage strategy, which greatly improves the storage and query efficiency of gridded images.

The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific articles. Here, we harness state-of-the-art machine learning techniques and develop a smart neuroscience metadata suggestion system accessible by both humans through a user-friendly graphical interface and machines via Application Programming Interface. We demonstrate a practical application to the public repository of neural reconstructions, NeuroMorpho.Org, thus expanding the existing web-based metadata management system currently in use. Quantitative analysis indicates that the suggestion system reduces personnel labor by at least 50%. Moreover, our results show that larger training datasets with the same software architecture are unlikely to further improve performance without ad-hoc heuristics due to intrinsic ambiguities in neuroscience nomenclature. All components of this project are released open source for community enhancement and extensions to additional applications. [ABSTRACT FROM AUTHOR]

The concept of data lake was proposed more than a decade ago. Although progress has been made in data lake research and applications, there are also numerous issues and challenges need to be addressed. In this paper, we survey some recent developments, provide our observations, as well as our expectations on future research and practice in this area. We start with a discussion on general terminology in data lakes, then review various aspects of data lakes, including an examination on metadata and related issues, information granularities involved in data lakes, unique features such as mixed lazy and eager approach in data lake lifecycle, relationship between data lakes and data mining, interplay between data lakes and data warehousing techniques, as well as some notable developments related to applications of data lakes. We present our observations while we examine these aspects, and wrap up this paper by expressing our hope of developing a unified framework for data lake lifecycle evaluation. [ABSTRACT FROM AUTHOR]