Event owner lens
Use this record to benchmark whether a comparable event makes the work after the room visible. The score is 36/100, so the next move is to benchmark the weakest pillars before repeating the format.
lead or directory source agenda analysis
VLDB
This lead or directory source in Academic / Research / Science shows 19 visible agenda rows from vldb.org and scores 36/100: a thin but inspectable design signal. The clearest public signals sit in Participation Architecture and Learning Transfer; the main limits are Follow Through and Network Design. Visible mechanisms include Participant work, Commitments, Feedback, and Baseline. Follow-through or tracking is at least visible enough to inspect, though causal proof still depends on stronger evidence. It is best... A practical reading: For a reader, this is mainly a warning or source-evidence record: it reads as a lead or directory source, with the strongest visible signal in participation architecture and learning transfer and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence. This page is an original public-evidence analysis, not a copy of the source agenda or an endorsement of the event. The score places the visible agenda in the thin outcome architecture band. The strongest visible pillars are Participation Architecture, Learning Transfer, and Problem Specificity; the thinnest visible pillars are Network Design, Follow Through, and Future-of-Work Fit. Visible mechanisms include Participant work, Commitments, Feedback, Baseline, and Network design. The extracted agenda preview includes 26 visible rows. The most common formats are Demo, Workshop, and Training; the most common inferred purposes are Showcase, Skill Building, and Participant Work.
Eight-pillar fingerprint
Hover any pillar to see what it measures and, where it scored low, what the agenda is missing.
Participation Architecture?77
Participation Architecture - 77/100. Participant work, contribution, interaction, and alternatives to passive broadcast.
Follow Through?16
Follow Through - 16/100. Owners, dates, commitments, progress checks, and accountability after the room.Missing: Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
Problem Specificity?45
Problem Specificity - 45/100. A clear costly problem, objective, decision, or performance target.
Personalization?42
Personalization - 42/100. Role, path, goal, preparation, or connection tailoring for participants.
Network Design?16
Network Design - 16/100. Structured weak ties, bridge-building, mixers, and relationship persistence.Missing: Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
Learning Transfer?49
Learning Transfer - 49/100. Applied practice, feedback, workplace use, refreshers, and 30-90 day transfer.
Evidence Maturity?39
Evidence Maturity - 39/100. Baseline, comparison, follow-up, isolation, and attribution confidence.
Future-of-Work Fit?34
Future-of-Work Fit - 34/100. Value against time, hybrid reality, accessibility, AI, and meeting load.Missing: Connect the agenda to modern work realities: hybrid participation, time value, accessibility, and AI-enabled support.
Fix the gaps
Field-tested exercises matched to this agenda's weakest pillars, from the exercise library.
Follow Through (16/100)
Exercises that strengthen it: Mental Toughness Workshop • WorkshopBank · 15% Solutions • WorkshopBank · Network Patches
Network Design (16/100)
Exercises that strengthen it: Fun Trust-Building Group Game for Team Connection - Izzat... · Exciting Group Trust Activity for Building Confidence... · PFP: September 2005
Personalization (42/100)
Exercises that strengthen it: Journey Mapping • WorkshopBank · Group Energiser & Perspective-Changing Exercise - Paradigm... · Photo Storytelling
Agenda Preview
The actual agenda we captured. Every block is classified by format and purpose. Open any block to see how we read it; the colored edge shows whether it is participant work, broadcast, logistics, or a showcase.
Room vs wrapper
81 percent of the 26 classified blocks put participants to work; the rest broadcast, show, or handle logistics. That mix is what drives the participation score.
Participant workBroadcastShowcaseLogistics
all eventOverall Program Structure Workshop Schedule (Days 1 & 5) Main Conference Schedule (Days 2, 3, 4)WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventKeynotesKeynoteExpert framing+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventIDSR Workshop](https://idsr-workshop.github.io)WorkshopParticipant work+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisParticipant work is implied by the formatInferred from format
all eventPhD Workshop Workshops Tutorials Demonstrations PanelsPanelDiscussion+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisNo participant output visible from this rowRead from source, no work signal
all eventVLDB 2025: Schedule of Papers and TutorialsTrainingSkill building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisParticipant work is implied by the formatInferred from format
all eventOnline Demos IDemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisOutcome inferred from formatInferred from format
all eventOnline Demos IIDemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisOutcome inferred from formatInferred from format
all eventOverall Program Structure Workshop Schedule (Days 1 & 5) Main Conference Schedule (Days 2, 3, 4)WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventKeynotesKeynoteThought Leadership+
Format · BroadcastKeynoteA featured talk from the stage. Builds awareness and energy, produces no participant output on its own.
Evidence basisMediumRead from source
all eventIDSR Workshop](https://idsr-workshop.github.io)WorkshopCo Creation+
Format · Participant workWorkshopParticipants work on a problem and produce something. The strongest signal of participation architecture.
Evidence basisMediumRead from source
all eventPhD Workshop Workshops Tutorials Demonstrations PanelsPanelDeliberation+
Format · BroadcastPanelExperts discuss while the audience watches. Surfaces perspective but rarely creates participant work.
Evidence basisMediumRead from source
all eventVLDB 2025: Schedule of Papers and TutorialsTrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventGiven the growing adoption of data-driven decision making, cloud data lakes are increasingly facing the need to support cost-effective Just-in-case'' archival over long time periods to meet legal and regulatory compliance requirements. Current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium--synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)--an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventMemory disaggregation (MD) allows for scalable and elastic data center design by separating compute (CPU) from memory. With MD, compute and memory are no longer coupled into the same server box. Instead, they are connected to each other via ultra-fast networking such as RDMA. MD can bring many advantages, e.g., higher memory utilization, better independent scaling (of compute and memory), and lower cost of ownership. This paper makes the case that MD can fuel the next wave of innovation on database systems. We observe that MD revives the great debate of "shared what" in the database community. We envision that distributed shared-memory databases (DSM-DB, for short) - that have not received much attention before - can be promising in the future with MD. We present a list of challenges and opportunities that can inspire next steps in system design making the case for DSM-DB.NetworkingRelationship Building+
Format · LogisticsNetworkingUnstructured mixing. Can carry incidental connection, but is not scored as designed network work.
Evidence basisMediumRead from source
all eventThis paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The framework employs background knowledge of the data collection process and biased data, working with or without limited statistics about the target population, to compute a range of answers for fairness queries. Using CRA, the framework builds predictive models that are certifiably fair on the target population, regardless of the availability of external data during training. The framework's efficacy is demonstrated through evaluations on real data, showing substantial improvement over existing state-of-the-art methods.TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventOnline Demos IDemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventCORNET: Learning Spreadsheet Formatting Rules By Example [demo] Mukul Singh (Microsoft); José Cambronero Sánchez (Microsoft); Sumit Gulwani (Microsoft Research); Vu Le (Microsoft); Carina Negreanu (Microsoft Research); Gust Verbruggen (Microsoft)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventADOps: An Anomaly Detection Pipeline in Structured Logs [demo] Xintong Song (Netease Fuxi AI Lab); Yusen Zhu (NetEase Fuxi AI Lab); Jianfei Wu (Netease Fuxi AI Lab); Bai Liu (Netease Fuxi AI Lab); Hongkang Wei (Netease Fuxi AI Lab)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventA Demonstration of DLBD: Database Logic Bug Detection System [demo] Xiu Tang (Zhejiang University); Sai Wu (Zhejiang Univ); Dongxiang Zhang (Zhejiang University); Ziyue Wang (Zhejiang University); Gongsheng Yuan (Zhejiang University); Gang Chen (Zhejiang University)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventDHive: Query Execution Performance Analysis via Dataflow in Apache Hive [demo] Chaozu Zhang (Southern University of Science and Technology); Qiaomu Shen (Southern University of Science and Technology); Bo Tang (Southern University of Science and Technology)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventLingua Manga: A Generic Large Language Model Centric System for Data Curation [demo] Zui Chen (Tsinghua University); Lei Cao (University of Arizona/MIT); Samuel Madden (Massachusetts Institute of Technology)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventData curation is a wide-ranging area which contains many critical but time-consuming data processing tasks. The diversity of data curation tasks makes it hard for building a general-purpose data curation system. To address this issue, we present Lingua Manga, a generic and user-friendly system that leverages pre-trained large language models. Lingua Manga is designed to enable flexible and swift development with automatic optimization to attain high performance and label efficiency. Through three example applications with different objectives and involving different types of users, we demonstrate that Lingua Manga can effectively assist both skilled programmers and low-code or even no-code users in solving data curation problems.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventInteractive Demonstration of EVA [demo] Gaurav Tarlok Kakkar (Georgia Institute of Technology); Aryan Rajoria (Georgia Institute of Technology); Myna Prasanna Kalluraya (Georgia Institute of Technology); Ashmita Raju (Georgia Institute of Technology); Jiashen Cao (Georgia Tech); Kexin Rong (Georgia Institute of Technology); Joy Arulraj (Georgia Tech)DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventIn this demonstration, we will present EVA, an end-to-end AI Relational database management system. We will demonstrate the capabilities and utility of EVA using three usage scenarios: (1) EVA serves as a backend for an exploratory video analytics interface developed using Streamlit and React, (2) EVA seamlessly integrates with the Python and Data Science ecosystems by allowing users to access EVA in a Python notebook alongside other popular libraries such as Pandas and Matplotlib, and (3) EVA facilitates bulk labeling with Label Studio, a widely-used labeling framework. By optimizing complex vision queries, we illustrate how EVA allows a wide range of application developers to harness the recent advances in computer vision.DemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
all eventAngel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent [industry] Xiaonan Nie (Peking University); Yi Liu (Tencent); Fangcheng Fu (Peking University); Jinbao Xue (Tencent); Dian Jiao (Tencent); Xupeng Miao (Carnegie Mellon University); Yangyu Tao (Tencent); Bin Cui (Peking University)TrainingSkill Building+
Format · Participant workTrainingGuided skill building where participants practice. Counts as participant work and learning transfer.
Evidence basisMediumRead from source
all eventOnline Demos IIDemoShowcase+
Format · Participant workDemoA hands-on or applied walkthrough that invites attendee questions and direct engagement.
Evidence basisMediumRead from source
The Full Reading
Why It Ranks This Way +
Calibrated from GES design 40/100 and verified 34/100, then capped for source appears to be a guide, directory, calendar, or ancillary page rather than a source agenda.
Reader Takeaway. For a reader, this is mainly a warning or source-evidence record: it reads as a lead or directory source, with the strongest visible signal in participation architecture and learning transfer and the biggest open question around follow through and network design. The practical test is whether the published agenda connects the room to post-event continuation and evidence.
Strongest signals: Participation Architecture, Learning Transfer, and Problem Specificity. Weakest signals: Network Design, Follow Through, and Future-of-Work Fit.
How This Agenda Could Improve +
- Replace generic networking blocks with designed introductions, ask-offer exchanges, peer groups, or bridge-building rituals.
- Add named owners, dates, implementation checkpoints, and a visible post-event continuation path.
- Connect the agenda to modern work realities: hybrid participation, time value, accessibility, and AI-enabled support.
Fastest next move: Add named owners, dated next steps, and a visible continuation path before treating the event as outcome-ready.
Role-Specific Reading +
Sponsor lens
Look beyond exposure. Strong sponsor value would show qualified interaction, problem work, buyer learning, customer evidence, or follow-up. The practical sponsor move is to look for structured introductions, buyer-seller fit, and relationship persistence.
Designer lens
The agenda is useful as a pattern sample from vldb.org. Redesign attention should go first to the lowest-scoring pillars; in practice, turn the thinnest agenda blocks into participant work.
Executive lens
Treat the visible agenda as an operating plan. The executive move is to require owners, dates, and evidence before treating the event as strategic. If owners, proof, and follow-through are not visible, the public record does not yet prove strategic movement.
Aggregator lens
Treat the source URL as evidence, not decoration. The data-product move is to keep it as lead evidence unless a direct agenda source is also available before ranking or syndicating the record.
What GES Means Here +
The Gathering Effectiveness Score is a strict 0-100 public-evidence reading of the agenda across eight pillars. It rewards visible participant work, follow-through, transfer, network design, and proof mechanisms more than polish, speaker fame, attendance, or satisfaction.
Visible mechanisms: Participant work, Commitments, Feedback, Baseline, Network design, Learning transfer, Personalization.
Evidence boundary: Scores reflect visible agenda/source evidence and should not be read as proof of causal event impact.
Limitations, Score Caps, and Review Flags +
Limitations
- Source shape weakens confidence in the agenda record.
Score caps
- 28: Source appears to be a guide, directory, calendar, or ancillary page rather than a source agenda.
Review flags
- Fourth-loop score cap: Source appears to be a guide, directory, calendar, or ancillary page rather than a source agenda.
- Source title indicates a registration, pass, or ticket page rather than an agenda surface.
- Agenda is useful as a source record but weak as evidence of gathering effectiveness.
- No tracking, validation, feedback, or impact measurement found in the visible source text.
- High cleanup rate: many extracted rows were hidden or merged as fragments.
Is this proof the event worked? +
No. This is a strict public-evidence reading of the agenda. Proof would require baseline, comparison, follow-up, attribution, and impact evidence beyond the listing.
What should a reader inspect first? +
Start with the source URL, then compare the eight pillar scores against the agenda rows. The biggest opportunities usually sit in follow-through, evidence maturity, and participant work.
Why publish weak records? +
Weak records are part of the map. They show where public agendas still describe sessions and speakers more often than outcomes, commitments, transfer, or proof.
How should I use the rows? +
Read the agenda rows as the visible design trace: formats, purposes, and evidence labels show what the public source made inspectable, not everything that happened in the room. This is a source-grounded interpretation of the public agenda record, not a copy of the source, and not an endorsement of the event.
Where To Go Next
Compare this agenda against other Academic / Research / Science events scored on the same eight pillars.