Towards Highly Adaptive Data-Intensive Systems: A Research Agenda

Abstract

Data-intensive software systems work in different contexts for different users with the aim of supporting heterogeneous tasks in heterogeneous environments. Most of the operations carried out by dataintensive systems are interactions with data. Managing these complex systems means focusing the attention to the huge amount of data that have to be managed despite limited capacity devices where data are accessed. This rises the need of introducing adaptivity in accessing data as the key element for data-intensive systems to become reality. Currently, these systems are not supported during their lifecycle by a complete process starting from design to implementation and execution while taking into account the variability of accessing data. In this paper, we introduce the notion of data-intensive self-adaptive (DISA) systems as dataintensive systems able to perform context-dependent data accesses. We define a classification framework for adaptation and we identify the key challenges for managing the complete lifecycle of DISA systems. For each problem we envisage a possible solution and we present the technological support for an integrated implementation.

Towards a knowledge driven framework for bridging the gap between software and data engineering

“…As software engineering above, the focus of these ontologies are on data engineering and data lifecycles. Two ontologies have been defined the data lifecycle ontology (DLO) 16 defined within ALIGNED and the DataID 17 ontology, defined by ALIGNED for the DBpedia association, for describing datasets.…”

“…While the origins of software engineering can be traced to the late 1960s [26], data engineering is a fairly new, though rapidly emerging discipline for (realtime) processing, curating, serving (via an API) and managing large volumes of data [16]. Additionally, recent years have also seen a significant increase in the demand for data-intensive, software applications that can efficiently handle large-scale sources of data [9].…”

“…https://w3id.org/slo 13 http://w3id.org/sip 14 http://se-on.org/ 15 purl.obolibrary.org/obo/swo.owl16 https://w3id.org/dlo 17 http://dataid.dbpedia.org/ns/core#…”