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Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines

Lilong Jiang, Protiva Rahman, Arnab Nandi

Tutorial presented at SIGMOD 2018 [pdf link]


Highly interactive query interfaces have become a popular tool for ad-hoc data analysis and exploration. Compared with traditional systems that are optimized for throughput or batched performance, ad-hoc exploration systems focus more on user-centric interactivity, which poses a new class of performance challenges to the backend. Further, with the advent of new interaction devices (e.g., touch, gesture) and different query interface paradigms (e.g., sliders, maps), maintaining interactive performance becomes even more challenging. Thus, when building and evaluating interactive data systems, there is a clear need to articulate the evaluation space.

In this paper, we describe unique characteristics of interactive workloads for a variety of user input devices and query interfaces. Based on a survey of literature in data interaction, we catalog popular metrics for evaluating such systems, highlight their deficiencies, and propose complementary metrics that allow us to provide a complete picture of interactivity. We motivate the need for behavior-driven optimizations of these interfaces and demonstrate how to analyze and employ user behavior for system enhancements through three scenarios that cover multiple device and interface combinations. Our case studies can inspire guidelines to help system designers design better interactive data systems, and can serve as a benchmark for evaluating systems that use these interfaces.


  title={Evaluating Interactive Data Systems: Workloads, Metrics, and Guidelines},
  author={Jiang, Lilong and Rahman, Protiva and Nandi, Arnab},
  booktitle={Proceedings of the 2018 International Conference on Management of Data},

Challenges in Evaluating Interactive Systems

There are two aspects to evaluating interactive systems: Performance and Usability.

System Factors

From a performance standpoint, interactive data infrastructures have higher requirements than traditional databases since these interfaces generate heavier workloads and there is a human waiting for immediate results on the other side.

  • Device Interface Variety: Each device-interface combination generates a different type of workload. For example, manipulating a map changes two predicates in a where clause: latitude and longitude. The workload generated by a range slider is much heavier than one generated by a text query interface, since typing takes more time than sliding. Further, querying on mouse and keyboard is more precise as opposed to querying with gestures which unnecessarily leads to heavier workloads due to jitter. The uniqueness of workload generated by each interface and the variety of interfaces make it difficult to create a standard set of benchmarks and optimizations.
  • Strict Latency Constraints: Compared to traditional databases which perform overnight batch processing, interactive systems have a human-in-the-loop who is iteratively querying and requires immediate feedback. This imposes strict latency constraints since a delay of more than just 100ms in displaying results can hinder the user's productivity.
  • Ambiguous Query Intent : The lack of haptic feedback and the sensor's ability to detect minor hand movements introduces imprecision into the querying process. This leads to the generation of unintended, noisy, and repeated queries being sent to the backend. In designing workloads, we need to account for the system's ability to deal with such unintended queries.
  • Dependent Queries : In interactive systems, adjacent queries, i.e. those issued in adjacent timestamps, are generated when the user performs continuous gestures. These queries usually return related or similar results since there is usually only a difference of one condition (e.g., where clause predicate) between them. This dependence can be used for performance optimizations.

Human Factors

The human-centric nature also means these systems require usability testing, to ensure the user is able to accomplish the desired tasks without being overwhelmed. The HCI community has a depth of literature on factors to consider when conducting usability tests, which include the following.

  • Learning: In a within-subject study, each user has to complete the same task on the system being tested as well as on the control. This leads to the user doing slightly better on the second system, due to being familiar with the task. In order to combat this, designers need to randomize the order in which the user is exposed to the system.
  • Fatigue: Long tasks can lead to users performing poorly towards the end, due to fatigue. Hence, tasks need to be broken into small chunks, with adequate breaks in between.
  • Carry-over effects: In within-subject studies, this refers to the user's performance on the second task being affected due to completing the first task. This differs from learning in that their performance in the second task could diminish. For example, if both the control and the experimental system are new to the user, they may perform poorly on the second by confusing functionalities with the first. Randomization can help account for this. However, if the effects are asymmetric, i.e. one user shows improvement while the other one shows deterioration, it makes it hard to draw conclusions.

Metrics Survey & Other Related Papers

130+ papers on data interaction [.bib file link]

LaTeX \cite{} version (click here to expand)

Query interface

  • Usability: Proxied by query specification or task completion time~\cite{jagadish2007making,nandi2013gestural, yang2007analysis, kandel2011wrangler, javed2012gravnav, abouzied2012dataplay, zhang2012network, satyanarayan2014lyra, basole2013understanding, yang2011clues, dimitriadou2014explore, igarashi2000speed, singh2012skimmer, bakke2011spreadsheet, liu2009spreadsheet}, number of iterations, navigation cost~\cite{li2007nalix, basu2008minimum, kashyap2010facetor, kashyap2011effective}, miss times~\cite{jiang2015snaptoquery}, number and uniqueness of insights~\cite{rzeszotarski2014kinetica, faith2007targeted, willett2007scented, singh2012skimmer}, accuracy \cite{rahman2017ve,siddiqui2016effortless}, ability to complete tasks~\cite{yang2007analysis, key2012vizdeck, basole2013understanding, cao2011dicon, yang2011clues, heer2008generalized, singh2012skimmer, liu2009spreadsheet}

  • Learnability: Ability to learn user actions with instructions: \cite{nandi2013gestural, rzeszotarski2014kinetica, basole2013understanding}

  • Discoverability: Ability to discover user actions without instructions: \cite{jiang2015snaptoquery, nandi2013gestural, bakke2011spreadsheet, martin1995high}

  • User feedback: Survey questions: \cite{nandi2013gestural, kandel2011wrangler, rzeszotarski2014kinetica, kamat2014distributed, willett2007scented, cao2011dicon, kashyap2010facetor, heer2008generalized, igarashi2000speed, liu2009spreadsheet}, case study~\cite{plaisant2004challenge, woodring2009multiscale, yuan2013dimension, yang2004value, bernard2013motionexplorer, seo2005rank, yang2003interactive, fisher2012trust, wei2012visual, ferreira2013visual, kosara2006parallel, mclachlan2008liverac}, design study~\cite{siddiqui2016effortless,sedlmair2012design},suggestions~\cite{biswas2013information, basole2013understanding, javed2012gravnav, satyanarayan2014lyra, wei2012visual, cao2011dicon, singh2012skimmer, khan2017data,rahman2017ve,siddiqui2016effortless,wongsuphasawat2016voyager,moritz2017trust}

  • Behavior analysis: Sequences of mouse press-drag-release~\cite{javed2012gravnav}, event state sequence~\cite{lam2007session}


  • Throughput: transactions / requests / tasks per second: TPC-C, TPC-E~\cite{tpc}, \cite{chan2008maintaining}

  • Latency: Captures the wait between issuing a query and getting back results: \cite{jiang2015snaptoquery, kamat2014distributed, liu2014effects, liu2013immens, fekete2002interactive, lins2013nanocubes, kashyap2010facetor, dimitriadou2014explore, chan2008maintaining,khan2017data, vartak2014seedb,rahman2017ve,siddiqui2016effortless, hellerstein1997online}

  • Scalability: performance with increasing data size, number of dimensions, number of machines: \cite{cooper2010benchmarking, liu2013immens, dimitriadou2014explore, kamat2014distributed,rahman2017ve,vartak2014seedb, kim2015rapid}

  • Cache hit rate: \cite{battle2015dynamic, kamat2014distributed, tauheed2012scout}

New Metrics

  • Latency Constraint Violation (LCV): A key metric which we find missing in prior work is perceived latency violations. Latency is a common metric used for measuring user wait times. However, in an interactive system, many queries are not issued in isolation: it can be dependent on the result of another query, causing cascading failures. Alternatively any latency introduced can break a user's "flow'' -- the user's experience diminishes if they perceive any latency at all. Hence, the goal for all systems should be zero perceivable latency. In order to capture this stricter notion, we introduce latency constraint violation. Specifically, this metric counts the number of times the zero latency rule is violated. LCV captures cases when the latency is within interactive bounds, but the user percieves a delay due to the dependencies.

  • Query Issuing Frequency (QIF): The high frame rate of interactive devices motivates the need to reduce the workload to the database. To capture this, we introduce query issuing frequency - a metric that measures the number of queries issued in a given time interval. There is a need for the QIF of the frontend to match the performance of the backend, otherwise the user experience is diminished.

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