The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis

Suntinger, Martin; Schiefer, Josef; Obweger, Hannes; Gröller, M. Eduard

doi:10.1109/pacificvis.2008.4475466

Cited by 29 publications

(34 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, other analysis tools focused on temporal categorical data such as [4], [18], [13] can benefit from TPS to provide sequential search capabilities. In addition, TPS can be used as an external function in database applications where searching for temporal patterns is required.…”

Section: Discussionmentioning

confidence: 99%

“…Understanding whether a particular pattern occurs, how frequently certain pattern occur, and whether one pattern occurs more often than the others are common questions an analyst would pursue. There has been much attention from both the academia and the industry to develop analysis tools focused on temporal events and their temporal patterns in a variety of domains: health care and electronic health records (EHR) [20], [4], business intelligence [18], and web server log analysis [13]. In particular, electronic health records have gained much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Temporal Pattern Search Algorithm for Personal History Event Visualization

Wang

Deshpande

Shneiderman

2012

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Abstract-We present Temporal Pattern Search (TPS), a novel algorithm for searching for temporal patterns of events in historical personal histories. The traditional method of searching for such patterns uses an automaton-based approach over a single array of events, sorted by time stamps. Instead, TPS operates on a set of arrays, where each array contains all events of the same type, sorted by time stamps. TPS searches for a particular item in the pattern using a binary search over the appropriate arrays. Although it is considerably more expensive per item, it allows TPS to skip many unnecessary events in personal histories. We show that TPS's running time is bounded by O(m 2 n lg(n)), where m is the number of items in a search pattern, and n is the number of events in a history. We also show that although the asymptotic running time of TPS is inferior to that of a non-deterministic finite automaton (NFA) approach (O(mn)), under our experimental conditions, TPS consistently outperforms NFA. Since the experimental conditions we describe here subsume the conditions under which users would typically use TPS (i.e. within an interactive visualization program), we argue that TPS is the appropriate design choice for us. Moreover, TPS and the strategy to split input data into several sorted arrays is generalizable to other applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Temporal Pattern Search Algorithm for Personal History Event Visualization

Wang

Deshpande

Shneiderman

2012

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…Existing business visualization tools primarily focus on specific business issues or contexts, such as business processes [43], knowledge management [13] and discovery [16], technological intelligence and forecasting [50], portfolio management [17], finance and stock markets [18,45,51], or patent analysis [27,28]. Most of these business visualization tools, however, simply act as measurement dashboards and not as exploratory systems.…”

Section: Related Workmentioning

confidence: 99%

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Basole¹,

Clear²,

Hu³

et al. 2013

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Fig. 1. The dotlink360 user interface including the navigator panel (left), the central visualization window, and the details panel (right).This image shows the Segment view with Apple selected so it and its market segments are colored yellow, while its partners are colored orange. The mouse cursor is over Nokia so it and its segments are colored light blue, with its partners dark blue. Company positions are determined by their segment memberships.Abstract-Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query-or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy.

show abstract

“…Analysis and mining tools [22][5] are integrated with the Event-Base for discovering causal relationships between events, mining events, finding patterns within events and they provide a facility for visualizing events, their relationships and patterns. Figure 3: The decrease of the business value of events over time according to [20] Current solutions that exist today, especially in the field of business intelligence and data mining, relating to the analysis of historical data and/or the appliance of OLAP, are capable of providing deeper insights into business operations.…”

Section: Event Analysismentioning

confidence: 99%

“…The Event Analyzer is a research effort, coupled to SARI and the Event-Base, that provides a toolset, for visualizing streams of events making use of tunnel metaphor [22]. The Event Analyzer is based on retrieving the desired information from the Event-Base through placing SARI-SQL queries against the EventBase.…”

Section: Display Parameters and Attributes Of An Eventmentioning

confidence: 99%