SHARQL: Shape Analysis of Recursive SPARQL Queries

Abstract

We showcase SHARQL, a system that allows to navigate SPARQL query logs, can inspect complex queries by visualizing their shape, and can serve as a back-end to flexibly produce statistics about the logs. Even though SPARQL query logs are increasingly available and have become public recently, their navigation and analysis is hampered by the lack of appropriate tools. SPARQL queries are sometimes hard to understand and their inherent properties, such as their shape, their hypertree properties, and their property paths are even more difficult to be identified and properly rendered. In SHARQL, we show how the analysis and exploration of several hundred million queries is possible. We offer edge rendering which works with complex hyperedges, regular edges, and property paths of SPARQL queries. The underlying database stores more than one hundred attributes per query and is therefore extremely flexible for exploring the query logs and as a back-end to compute and display analytical properties of the entire logs or parts thereof.

A Trichotomy for Regular Trail Queries

“…This condition is trivial to check and also captures languages outside SP tract such as (ab) * and a * bc * . Moreover, the condition seems to be useful: we analyzed the 50 million RPQs found in the logs of [BMT18] and discovered that over 99.8% of the RPQs are single-occurrence. • In terms of closure properties, learnability, or complexity of testing if a given regular language belongs to SP tract or T tract , the classes seem to behave the same.…”

“…Furthermore, every single-occurrence regular expression [BNSV10] is in T tract , which can be a convenient guideline for users of graph databases, since singleoccurrence (every alphabet symbol occurs at most once) is a very simple syntactical property. It is also popular in practice: we analyzed the 50 million RPQs found in the logs of [BMT18] and discovered that over 99.8% of the RPQs are single-occurrence regular expressions.…”

The Design of Triple Store and Query Processing on GPU for Large Scale Resource Description Framework Data

“…, [5] การใช้ คุ ณสมบั ติ หรื อรู ปร่ างของข้ อมู ล ช่ วยในการค้ นคื นข้ อมู ล [6], [7] การออกแบบและพั ฒนา ระบบโดยใช้ ตั วเร่ งฮาร์ ดแวร์ (Hardware Accelerator) ใน การลดเวลาคิ วรี [8], [9] หรื อสถาปั ตยกรรมที ่ มี ตั ว ประมวลผลหลายชนิ ด (Heterogenous Architecture) [10] และงานวิ จั ยที ่ ใช้ เฟรมเวิ ร์ คสํ าหรั บระบบข้ อมู ลขนาดใหญ่ (Big Data) เป็ นพื ้ นฐานของการสร้ างระบบจั ดเก็ บและคิ วรี ข้ อมู ล [11], [12] ในงานวิ…”