DIACHRON 2016 - 2nd International Workshop on Preservation of Evolving Big Data

Recently, there has been a vast and rapidly increasing quantity of scientific, corporate, government and crowd-sourced knowledge bases published and curated independently on the Data Web in various data formats, such as Linked Open Data (LOD) and Data APIs. This great abundance of open Big Data sources and the need for Web-scale knowledge management support is essentially transforming the Web into a vibrant information ecosystem. One of its main characteristics is its dynamicity and its decentralized evolution that spans across multiple interrelated sources and data hubs. Traditional closed-world settings impose that data management and evolution are performed within a well-defined controlled environment where change operations and dependencies on data can be easily monitored and handled. On the other hand, web and digital preservation techniques assume that preservation subjects, such as web pages, are plain digital assets that are collected (usually via a crawling mechanism) and individually archived for future reference. In contrast with these two approaches, the Data Web poses new requirements for revisiting and adjusting traditional closed-world data management techniques such as temporal management and change detection, data archiving and preservation, data ingestion, integration and enrichment, data provenance and quality, data visualization and exploratory analysis to the characteristics of multi-curated knowledge bases.