A hierarchical aggregation framework for efficient multilevel visual exploration and analysis

Abstract

Abstract. Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed methods. The presented model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we provide a performance evaluation and a empirical user study employing real datasets.

Using knowledge anchors to facilitate user exploration of data graphs

“…Facet Graphs [38], gFacet [39], and tFacet [40], are early efforts in this area. More recent attempts include Rhizomer [41], which combines navigation menus and maps to provide flexible exploration between different classes; Facete [42], a visualization-based exploration tool that offers faceted filtering functionalities; Hippalus [43], which allows users to rank the facets according to their preferences; Voyager [44], which couples faceted browsing with visualization recommendation to support users exploration; and SynopsViz [45], which provides faceted browsing and filtering RDF over classes and properties. Although these approaches provide support for user exploration, layman users who are performing exploratory search tasks to learn or investigate a new topic, can be cognitively overloaded, especially when the facets provide many options (i.e.…”

HiePaCo: Scalable Hierarchical Exploration in Abstract Parallel Coordinates Under Budget Constraints

“…Elqvist et al [13] provided general guidelines for multiscale representations and interactions, based on such hierarchical aggregation. Bikakis et al [30] presented a framework for hierarchical aggregation oriented towards the computational aspects of hierarchical navigation. Interactively changing the level of detail can be integrated into different ways (see [31] for a general review).…”

Specification and implementation of mapping rule visualization and editing: MapVOWL and the RMLEditor

“…Regardless of the used visualization, research is being done to improve the support for large datasets. For example, Bikakis et al [4] introduce a generic model for organizing, and analyzing numeric and temporal data in a multilevel fashion to deal with the challenges that come with the use of large datasets, such as information overload. The model is not tied to a specific visualization and, thus, it can be used with any of the aforementioned tools to improve the support for such datasets.…”

“…Nowadays Linked Data still stems from (semi-)structured formats. A few of the most well-known and larger Linked Data sets 2 [23] are: DBpedia dataset 3 [6], with approximately 1.39 billion triples derived from Wikipedia 4 where the data is originally represented in the wikitext syntax; Linked Geo Data 5 [58] with approximately 1.38 billion triples derived from Open Street Map planet files loaded in multiple databases; UniProt [12] (UniPro-tKB, Uniref and UniParc), with approximately 45 billion triples across 3 datasets derived from the UniProt Knowledgebase 6 ; and Bio2rdf 7 [2], with approximately 11 billion triples across 35 datasets.…”