Visual graph query interfaces (VQI) facilitate non-programmers to query graph data effortlessly. The construction of these interfaces for large networks is typically not data-driven. That is, they do not exploit the underlying networks to automatically generate the contents of various panels of a VQI. Such data-driven construction has several benefits such as facilitating efficient top-down and bottom-up query formulation and portability of an interface across different application domains and sources. In this demonstration, we present a novel plug-and-play visual subgraph query interface construction engine called PLAYPEN that can be plugged on any large network G with a plug specification b to automatically generate the VQI for G that satisfies b by populating various components of the interface.

The database systems course is offered in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in an RDBMS in practice. To this end, comprehension of the impact of various physical operators on the selected query execution plan (QEP) of a query is paramount. Unfortunately, off-the-shelf RDBMS typically only expose the QEP to users without revealing information about the impact of alternative choices of various physical operators on it in a user-friendly manner to aid learning. In this demonstration, we present a novel system called MOCHA that facilitates exploration and visualization of the impact of alternative physical operator choices on the QEP of a given SQL query. MOCHA accepts an SQL query as input, and compares and visualizes the QEP and alternative plans which are selected based on learner-specified operator preferences. Furthermore, it intuitively explains why the key operators in a QEP are chosen by connecting them to established knowledge in the literature.

Several visual graph query interfaces (a.k.a gui) expose a set of canned patterns (i.e., small subgraph patterns) to expedite subgraph query formulation by enabling pattern-at-a-time construction. Unfortunately, manual generation of canned patterns is not only labour intensive but also may lack diversity to support efficient visual formulation of a wide range of subgraph queries. Recent efforts have taken a data-driven approach to select high-quality canned patterns for a gui automatically from the underlying graph database. However, as the underlying database evolves, these selected patterns may become stale and adversely impact efficient query formulation. In this paper, we present a novel framework called Midas for efficient and effective maintenance of the canned patterns as the database evolves. Specifically, it adopts a selective maintenance strategy that guarantees progressive gain of coverage of the patterns without sacrificing their diversity and cognitive load. Experimental study with real-world datasets and visual graph interfaces demonstrates the effectiveness of Midas compared to static guis.

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

Several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (eg. chemical compounds) provide visual graph query interfaces (a.k.a GUI) to facilitate non-programmers to query these sources. However, construction of these visual interfaces is not data-driven. That is, it does not exploit the underlying data graphs to automatically generate the contents of various panels in a GUI. Such data-driven construction has several benefits such as facilitating efficient subgraph query formulation and portability of the interface across different application domains and sources. In this demonstration, we present a novel data-driven visual subgraph query interface construction engine called AURORA. Specifically, given a graph repository D containing a collection of small- or medium-sized data graphs, it automatically generates the GUI for D by populating various components of the interface. We demonstrate various innovative features of AURORA.

The paradigm of interleaving (i.e. blending) visual subgraph query formulation and processing by exploiting the latency offered by the GUI brings in several potential benefits such as superior system response time (SRT) and opportunities to enhance usability of graph databases. Recent efforts at implementing this paradigm are focused on subgraph isomorphism-based queries, which are often restrictive in many real-world graph applications. In this demonstration, we present a novel system called BOOMER to realize this paradigm on more generic but complex bounded 1-1 p-homomorphic(BPH) queries on large networks. Intuitively, a BPH query maps an edge of the query to bounded paths in the data graph. We demonstrate various innovative features of BOOMER, its flexibility, and its promising performance.

Smart contracts enable developers to run instructions on blockchains (eg. Ethereum) and have broad range of real-world applications. Solidity is the most popular high-level smart contract programming language on Ethereum. Coding in such language, however, demands a user to be proficient in contract programming and debugging to construct smart contracts correctly. In practice, such expectation makes it harder for non-programmers to take advantage of smart contracts. In this demonstration, we present a novel visual smart contract construction system on Ethereum called latte to make smart contract development accessible to non-programmers. Specifically, it allows a user to construct a contract without writing Solidity code by manipulating visual objects in a direct manipulation-based interface. Furthermore, latte interactively guides users and makes them aware of the cost (in units of Gas) of visual actions undertaken by them during contract construction.

Visual graph query interfaces (a.k.a gui ) widen the reach of graph querying frameworks across different users by enabling non-programmers to use them. Consequently, several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (\textite.g., chemical compounds) provide such visual interfaces. Majority of these interfaces expose a fixed set ofcanned patterns (\textiti.e., small subgraph patterns) to expedite query formulation by enabling pattern-at-a-time in lieu of edge-at-a-time construction mode. Canned patterns to be displayed on a gui are typically selected manually based on domain knowledge. However, manual generation of canned patterns is labour intensive. Furthermore, these patterns may not sufficiently cover the underlying data graphs to expedite visual formulation of a wide range of subgraph queries. In this paper, we present a generic and extensible framework called Catapult to address these limitations. Catapult takes a data-driven approach toautomatically select canned patterns, thereby taking a concrete step towards the vision of data-driven construction of visual query interfaces. Specifically, it firstclusters the underlying data graphs based on their topological similarities and thensummarize each cluster to create acluster summary graph (csg ). The canned patterns within a user-specifiedpattern budget are then generated from these csg s by maximizingcoverage anddiversity, and minimizingcognitive load of the patterns. Experimental study with real-world datasets and visual graph interfaces demonstrates the superiority of Catapult compared to traditional techniques.

Visual graph query interfaces (a.k.a GUI) make it easy for non-expert users to query graphs. Recent research has laid out and implemented a vision of a novel subgraph query processing paradigm where the latency offered by the GUI is exploited to blend visual query construction and processing by generating and refining candidate result matches iteratively during query formulation. This paradigm brings in several potential benefits such as superior system response time (srt) and opportunities to enhance usability of graph databases. However, these early efforts focused on subgraph isomorphism-based graph queries where blending is performed by iterative edge-to-edge mapping. In this paper, we explore how this vision can be realized for more generic but complex 1-1 p-homomorphic p-hom) queries introduced by Fan et al. A 1-1 p-hom query maps an edge of the query to paths in the data graph. We present a novel framework called BOOMER for blending bounded 1-1 p-hom (bph ) queries, a variant of 1-1 p-hom where the length of the path is bounded instead of arbitrary length. Our framework is based on a novel online , adaptive indexing scheme called cap index. We present two strategies for CAP index construction, immediate and deferment-based, and show how they can be utilized to facilitate judicious interleaving of visual bph query formulation and query processing. BOOMER is also amenable to modifications to a bph query during visual formulation. Experiments on real-world datasets demonstrate both efficiency and effectiveness of Boomer for realizing the visual querying paradigm on an important type of graph query.

Visual graph interfaces improve the usability of graph databases by making it easier for users to formulate queries. Recently, a variety of interactive query formulation-based techniques (e.g., blending of visual query construction and processing, visual query suggestions) have been proposed to enhance query performance and usability. Comprehensive user studies are needed to exhaustively and systematically evaluate performance of the proposed techniques, but, unfortunately, user studies are expensive and time consuming. To reduce the cost and time needed, we present a novel synthetic visual subgraph query simulator called VISUAL. VISUAL realistically simulates subgraph query construction without requiring human users. It can automatically generate test subgraph queries having different user-specified characteristics by utilizing the underlying indexes and simulate their formulation based on different query formulation sequences. A key feature of this simulator is that it is built on top of an HCI-inspired, extensible quantitative model which enables us to model the visual query formulation process quantitatively. Our experimental study demonstrates the effectiveness of VISUAL in accurately simulating visual subgraph queries.

In [3], we laid out the vision of a novel graph query processing paradigm, where visual subgraph query formulation is interleaved (or “blended”) with query processing by exploiting the latency offered by the gui. Our recent attempts at implementing this vision [6], [7] do not provide any robust framework to systematically investigate the performance of this novel paradigm. This is because it is prohibitively expensive to engage a large number of users to formulate a large number of visual queries in order to measure the performance of blending query formulation with query processing. In this demonstration, we present a novel synthetic visual subgraph query simulator called ViSual that can evaluate the performance of this paradigm for a large number of visual subgraph queries without requiring a large number of users to formulate them. Specifically, it leverages principles from hci to quantify the gui latency that is necessary to realistically simulate blending of query formulation and query processing.