SPICE in-memory engine
E426149
SPICE in-memory engine is Amazon QuickSight’s high-performance, columnar, in-memory data store designed to enable fast, scalable, and interactive analytics on large datasets.
All labels observed (1)
| Label | Occurrences |
|---|---|
| SPICE in-memory engine canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4280084 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SPICE in-memory engine Context triple: [Amazon QuickSight, supportsFeature, SPICE in-memory engine]
-
A.
xVelocity in-memory analytics engine
xVelocity in-memory analytics engine is a columnar, in-memory data processing engine developed by Microsoft to enable fast, compressed, and scalable analytical querying for business intelligence tools.
-
B.
SPARC microprocessor architecture
The SPARC microprocessor architecture is a RISC-based instruction set architecture widely used in high-performance and enterprise servers, originally created to power scalable, multi-processor systems.
-
C.
Xe-HPG microarchitecture
Xe-HPG microarchitecture is Intel’s high-performance gaming-oriented GPU architecture designed to power its discrete Arc graphics cards with advanced features like hardware-accelerated ray tracing.
-
D.
Goya inference processor
The Goya inference processor is Habana Labs’ specialized AI chip designed to accelerate deep learning inference workloads with high performance and efficiency.
-
E.
Spike RISC-V ISA simulator
Spike RISC-V ISA simulator is the official reference software simulator for the RISC-V instruction set architecture, used to validate and test RISC-V implementations.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SPICE in-memory engine Target entity description: SPICE in-memory engine is Amazon QuickSight’s high-performance, columnar, in-memory data store designed to enable fast, scalable, and interactive analytics on large datasets.
-
A.
xVelocity in-memory analytics engine
xVelocity in-memory analytics engine is a columnar, in-memory data processing engine developed by Microsoft to enable fast, compressed, and scalable analytical querying for business intelligence tools.
-
B.
SPARC microprocessor architecture
The SPARC microprocessor architecture is a RISC-based instruction set architecture widely used in high-performance and enterprise servers, originally created to power scalable, multi-processor systems.
-
C.
Xe-HPG microarchitecture
Xe-HPG microarchitecture is Intel’s high-performance gaming-oriented GPU architecture designed to power its discrete Arc graphics cards with advanced features like hardware-accelerated ray tracing.
-
D.
Goya inference processor
The Goya inference processor is Habana Labs’ specialized AI chip designed to accelerate deep learning inference workloads with high performance and efficiency.
-
E.
Spike RISC-V ISA simulator
Spike RISC-V ISA simulator is the official reference software simulator for the RISC-V instruction set architecture, used to validate and test RISC-V implementations.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Amazon QuickSight component
ⓘ
columnar data store ⓘ in-memory analytics engine ⓘ |
| abbreviationOf | Super-fast, Parallel, In-memory Calculation Engine ⓘ |
| dataModel | columnar ⓘ |
| designedFor |
business intelligence
ⓘ
interactive analytics ⓘ |
| developedBy | Amazon Web Services NERFINISHED ⓘ |
| enables |
fast dashboard loading
ⓘ
offline query execution from source systems ⓘ scalable analytics ⓘ sub-second query response in many cases ⓘ |
| featureOf |
Amazon QuickSight Enterprise Edition
NERFINISHED
ⓘ
Amazon QuickSight Standard Edition NERFINISHED ⓘ |
| integratesWith |
AWS Glue Data Catalog
NERFINISHED
ⓘ
AWS data sources ⓘ Amazon RDS NERFINISHED ⓘ Amazon Redshift NERFINISHED ⓘ Amazon S3 NERFINISHED ⓘ Athena NERFINISHED ⓘ external SQL databases ⓘ |
| marketedAs | Super-fast, Parallel, In-memory Calculation Engine ⓘ |
| optimizedFor |
fast aggregation
ⓘ
high concurrency ⓘ low-latency queries ⓘ |
| partOf | Amazon QuickSight NERFINISHED ⓘ |
| storageType | in-memory ⓘ |
| supports |
ad hoc analysis
ⓘ
dashboarding ⓘ data exploration ⓘ large datasets ⓘ |
| supportsFeature |
aggregations
ⓘ
calculated fields evaluation ⓘ columnar compression ⓘ data compression ⓘ data indexing ⓘ data partitioning ⓘ incremental data refresh ⓘ joins across imported datasets ⓘ materialization of imported data ⓘ row-level security enforcement at query time ⓘ scheduled data refresh ⓘ time-series calculations ⓘ |
| usedBy |
Amazon QuickSight analyses
ⓘ
Amazon QuickSight dashboards NERFINISHED ⓘ Amazon QuickSight paginated reports ⓘ |
| usedFor |
improving query performance in QuickSight
ⓘ
reducing load on underlying data sources ⓘ supporting many concurrent QuickSight users ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: SPICE in-memory engine Description of subject: SPICE in-memory engine is Amazon QuickSight’s high-performance, columnar, in-memory data store designed to enable fast, scalable, and interactive analytics on large datasets.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.