Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1907.06946

A Subjective Interestingness measure for Business Intelligence explorations

Alexandre Chanson·Ben Crulis·Nicolas Labroche·Patrick Marcel
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper addresses the problem of defining a subjective interestingness measure for BI exploration. Such a measure involves prior modeling of the belief of the user. The complexity of this problem lies in the impossibility to ask the user about the degree of belief in each element composing their knowledge prior to the writing of a query. To this aim, we propose to automatically infer this user belief based on the user's past interactions over a data cube, the cube schema and other users past activities. We express the belief under the form of a probability distribution over all the query parts potentially accessible to the user, and use a random walk to learn this distribution. This belief is then used to define a first Subjective Interestingness measure over multidimensional queries. Experiments conducted on simulated and real explorations show how this new subjective interestingness measure relates to prototypical and real user behaviors, and that query parts offer a reasonable proxy to infer user belief.

Explore related subjects

cs.DB

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alexandre Chanson, Ben Crulis, Nicolas Labroche, Patrick Marcel. 2019-07-16. A Subjective Interestingness measure for Business Intelligence explorations. https://arxiv.org/abs/1907.06946

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Towards Effective Orchestration of AI x DB Workloads

AI-driven analytics are increasingly crucial to data-centric decision-making. Executing relational and AI operators in separate runtimes prevents the database optimizer and runtime from coordinating operator ordering, model placement, batching, and state reuse. Integrating AI operators into database engines enables such coordination but raises challenges in jointly optimizing query processing and model execution, scheduling under resource contention, and reusing relational intermediates and AI artifacts. This paper formalizes AIxDB workloads as iterative, concurrent, and shareable executions that interleave relational and AI operators. We then advocate database-native orchestration as a paradigm for redesigning database engines for these workloads and distill two design principles: holistic AIxDB co-optimization and unified AIxDB cache management. We present NeurEngine as a proof-of-concept prototype and report preliminary results illustrating the performance benefits of database-native orchestration

cs.DB↗
arXiv2026

A Functional Pilot for Certified Freshness-Aware Semantic--Spatial Range Retrieval

Geographic applications need every object inside a radius that satisfies a semantic threshold, yet embedding indexes return approximate top-ranked lists and may omit qualifying records silently. We present FRESH-GEORANGE, a semantic- spatial range design that separates source-watermark freshness from optional record age. Geographic cells and semantic mi- croblocks provide admissible pruning bounds; a graph proposes verification order but supplies no correctness evidence. Exact mode scans every nonprunable block and the delta overlay. Certified mode may stop early and reports a deterministic query- specific recall lower bound from verified answers and unresolved records. A reproducible CPU pilot uses 2,500 real OpenFlights airport records, a 2,000-record base, and 740 simulated insert, delete, and text-revision events; it evaluates 180 unique queries over five seeds. Exact mode achieved 100.00% set recall on every query. The 95-percent mode achieved 99.91% empirical mean recall with a 99.41% reported mean certificate and no observed bound violation. However, its 7.24 ms median latency was 5.85 times the 1.24 ms spatial-first exact baseline, and full-history delta replay became slower than rebuilding at larger batches. The prototype therefore validates the completeness mechanism, not performance superiority or production freshness. Submission- scale evaluation requires real map diffs, official recent baselines, and truly incremental versioned maintenance.

cs.DB↗
arXiv2026

AkasicMEM: Governed Enterprise Memory for Agents

Agent memory enables enterprise agents to retain knowledge acquired during work and reuse it across tasks and agents, turning execution experience into persistent organizational knowledge. Realizing this potential requires both source--memory integration, through which enterprise sources and accumulated memory can be utilized together, and memory governance, through which shared memory remains subject to organizational policies throughout its lifecycle. These requirements interact when information from enterprise sources persists in memory. As this information is repeatedly derived and reused under changing principals and policies, source restrictions may be bypassed, resulting in information leakage. Preventing such leakage requires authorization continuity, under which source restrictions remain effective throughout source-to-memory and memory-to-memory derivation and reuse. Existing approaches address these concerns individually, but do not treat source--memory integration, memory governance, and authorization continuity as combined core design targets across the memory lifecycle. We define Governed Enterprise Memory as agent memory designed around this combined scope and present AkasicMEM as its realization. AkasicMEM realizes authorization continuity through transitive lineage, policy composition during memory formation, and policy re-evaluation during retrieval. It is built on GraphAI's AkasicDB, a unified vector--graph--relational database whose storage and execution substrate enables the underlying operations of these mechanisms to be jointly optimized and executed.

cs.DB↗