Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate whether progressively refined intermediate results, accompanied by quality estimates, can enable users to decide whether to accept partial results or continue processing. We evaluate this approach within the Multidimensional Media Model, where media collections are explored through metadata tagsets and hierarchies. Our study analyses operator placement, showing that moving deduplication and grouping from the database to the server reduces time-to-first-result by over 90% while maintaining steady quality improvement. We further examine join ordering and selectivity estimation for reliable progress prediction. Our results demonstrate that online aggregation principles can substantially improve responsiveness in multimedia exploration systems.

The ACM Lifelog Search Challenge (LSC) is an annual comparative benchmarking exercise that brings together researchers in the field of multimedia retrieval to evaluate interactive search systems using a large-scale multimodal lifelog dataset. This paper presents an overview of the ninth edition of the challenge (LSC’26), held as a workshop during the ACM International Conference on Multimedia Retrieval (ICMR ’26) in Amsterdam. To broaden its scope, the workshop now features three submission tracks: the traditional Challenge Track for real-time search performance, a new General Lifelog Research Track for theoretical and architectural advancements, and an additional Open Source Track aimed at enhancing reproducibility and reducing barriers to entry for new participants.

For the eighth time since 2018, the ACM Lifelog Search Challenge (LSC) was run to compare interactive lifelog search systems in a live metrics-based challenge. The goal of the LSC workshop is to comparatively evaluate the capabilities of systems accessing a large multimodal lifelog. LSC'25 attracted eleven participating teams, each of which had developed an innovative interactive lifelog retrieval system. The benchmark was organised in a hybrid manner (due to political issues in 2025) at the LSC workshop at ACM ICMR'25 in Chicago, USA. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

Tracking the evolution of smart contracts is challenging due to their immutable nature and complex upgrade mechanisms. We introduce EvoChain, a comprehensive framework and dataset designed to track and visualize smart contract evolution. Building upon data from our previous empirical study, EvoChain models contract relationships using a Neo4j graph database and provides an interactive web interface for exploration. The framework consists of a data layer, an API layer, and a user interface layer. EvoChain allows stakeholders to analyze contract histories, upgrade paths, and associated vulnerabilities by leveraging these components. Our dataset encompasses approximately 1.3 million upgradeable proxies and nearly 15,000 historical versions, enhancing transparency and trust in blockchain ecosystems by providing an accessible platform for understanding smart contract evolution.

For the seventh time since 2018, the Lifelog Search Challenge (LSC) benchmarked interactive lifelog search systems in a live challenge. The LSC goal is to comparatively evaluate system capabilities to access large multimodal lifelogs comprising hundreds of thousands of records. LSC'24 attracted an unprecedented record number of twenty-one participating teams, where each team proposes innovative ideas implemented to new or already established interactive lifelog retrieval systems. The benchmark was organised in front of a live audience at the LSC workshop at ACM ICMR'24 in Phuket, Thailand. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

We present the 3D Media Search Engine (3DMSE), which is designed to facilitate the exploration and retrieval of 3D models and images. 3DMSE incorporates unimodal, cross-modal and multimodal retrieval, using any combinations of mesh, point-cloud and multi-image representations. The 3DMSE system is built on the recently proposed MuseHash approach for multimodal representation, and offers a user-friendly web interface that enables formulating queries, presenting search results, and visualising 3D information in an accessible manner.

Different task interpretations are a highly undesired element in interactive video retrieval evaluations. When a participating team focuses partially on a wrong goal, the evaluation results might become partially misleading. In this paper, we propose a process for refining known-item and open-set type queries, and preparing the assessors that judge the correctness of submissions to open-set queries. Our findings from recent years reveal that a proper methodology can lead to objective query quality improvements and subjective participant satisfaction with query clarity.

For the sixth time since 2018, the Lifelog Search Challenge (LSC) was organized as a comparative benchmarking exercise for various interactive lifelog search systems. The goal of this international competition is to test system capabilities to access large multimodal lifelogs. LSC’23 attracted twelve participanting teams, each of whom had developed a competitive interactive lifelog retrieval system. The benchmark was organized in front of live audience at the LSC workshop at ACM ICMR’23. As in previous editions, this introductory paper presents the LSC workshop and introduces the participating lifelog search systems.

This paper presents a novel method for supporting multiple modalities in the field of image retrieval, called Multimodal Bayesian Supervised Hashing (MuseHash). The method takes into consideration the semantic information of the training data through the use of Bayesian regression to estimate the semantic probabilities and statistical properties in the retrieval process. MuseHash is an extension of the previously proposed Bayesian ridge-based Semantic Preserving Hashing (BiasHash) method. Experimentation on various domain-specific and benchmark datasets demonstrates that MuseHash outperforms seven existing state-of-the-art methods in image retrieval performance, regardless of the feature extractor type, code length, and visual or textual descriptors used. This highlights the robustness and adaptability of MuseHash, making it a promising solution for multimodal image retrieval.

In this paper we describe the latest iteration of the Virtual Reality Multimedia Analytics (ViRMA) system, a novel approach to multimedia analysis in virtual reality which is supported by the Multi-dimensional Multimedia Model.

For the fifth time since 2018, the Lifelog Search Challenge (LSC) facilitated a benchmarking exercise to compare interactive search systems designed for multimodal lifelogs. LSC'22 attracted nine participating research groups who developed interactive lifelog retrieval systems enabling fast and effective access to lifelogs. The systems competed in front of a hybrid audience at the LSC workshop at ACM ICMR'22. This paper presents an introduction to the LSC workshop, the new (larger) dataset used in the competition, and introduces the participating lifelog search systems.

The Lifelog Search Challenge (LSC) is an annual benchmarking challenge for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'21, the fourth challenge, attracted sixteen participants, each of which had developed interactive retrieval systems for large multimodal lifelogs. These interactive retrieval systems participated in a comparative evaluation in front of an online live-audience at the LSC workshop at ACM ICMR'21. This overview presents the motivation for LSC'21, the lifelog dataset used in the competition, and the participating systems.

User Relevance Feedback (URF) is a class of interactive learning methods that rely on the interaction between a human user and a system to analyze a media collection. To improve URF system evaluation and design better systems, it is important to understand the impact that different interaction strategies can have. Based on the literature and observations from real user sessions from the Lifelog Search Challenge and Video Browser Showdown, we analyze interaction strategies related to (a) labeling positive and negative examples, and (b) applying filters based on users' domain knowledge. Experiments show that there is no single optimal labeling strategy, as the best strategy depends on both the collection and the task. In particular, our results refute the common assumption that providing more training examples is always beneficial: strategies with a smaller number of prototypical examples lead to better results in some cases. We further observe that while expert filtering is unsurprisingly beneficial, aggressive filtering, especially by novice users, can hinder the completion of tasks. Finally, we observe that combining URF with filters leads to better results than using filters alone.

The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.

Indexing and retrieving music based on emotion is a powerful retrieval paradigm with many applications. Traditionally, studies in the field of music emotion recognition have focused on training and testing supervised machine learning models using a single music dataset. To be useful for today's vast music libraries, however, such machine learning models must be widely applicable beyond the dataset for which they were created. In this work, we analyze to what extent models trained on one music dataset can predict emotion in another dataset constructed using a different methodology, by conducting cross-dataset experiments with three publicly available datasets. Our results suggest that training a prediction model on a homogeneous dataset with carefully collected emotion annotations yields a better foundation than prediction models learned on a larger, more varied dataset, with less reliable annotations.

In today's world of ever-increasing multimedia collections, dynamically and persistently maintaining high-dimensional indexes is imperative for industrial applications. Since HDD performance is the main bottleneck in index maintenance, we investigate the impact of SSD technology. We use the NV-tree to drive our analysis, as the only high-dimensional index in the literature which has seriously addressed updates. Our simulation model indicates that an index of 1.5 billion descriptors can be built dynamically on a high-end SSD in just over four hours of disk time, which is more than 500x faster than using a high-end HDD. Relatively small investment in the new SSD technology can thus make dynamic and persistent high-dimensional indexes very feasible.

This paper presents Blackthorn, an efficient interactive multimodal learning approach facilitating analysis of multimedia collections of 100 million items on a single high-end workstation. This is achieved by efficient data compression and optimizations to the interactive learning process. The compressed i-I64 data representation costs tens of bytes per item yet preserves most of the visual and textual semantic information. The optimized interactive learning model scores the i-I64-compressed data directly, greatly reducing the computational requirements. The experiments show that Blackthorn is up to 105x faster than the conventional relevance feedback baseline. Blackthorn is shown to vastly outperform the baseline with respect to recall over time. Blackthorn reaches up to 92% of the precision achieved by the baseline, validating the efficacy of the i-I64 representation. On the YFCC100M dataset, Blackthorn performes one complete interaction round in 0.7 seconds. Blackthorn thus opens multimedia collections comprising 100 million items to learning-based analysis in fully interactive time.

This paper presents the NV-Tree (Nearest Vector Tree). It addresses the specific, yet important, problem of efficiently and effectively finding the approximate k-nearest neighbors within a collection of a few billion high-dimensional data points. The NV-Tree is a very compact index, as only six bytes are kept in the index for each high-dimensional descriptor. It thus scales extremely well when indexing large collections of high-dimensional descriptors. The NV-Tree efficiently produces results of good quality, even at such a large scale that the indices cannot be kept entirely in main memory any more. We demonstrate this with extensive experiments using a collection of 2.5 billion SIFT (Scale Invariant Feature Transform) descriptors.

It has never been so easy to take pictures, and personal image collections have never been so large. Unfortunately, most current photo browsers provide very limited support for effectively navigating image collections. This demonstration proposal describes PhotoCube, a personal image browser based on a multi-dimensional data model similar to the model used in OLAP applications. With PhotoCube, users can tag pictures, structure tags into various hierarchies, and browse images according to any possible perspective. We also describe three demonstration scenarios that show the power, flexibility and scalability of PhotoCube.

It is amazingly easy to get meaningless results when measuring flash devices, partly because of the peculiarity of flash memory, but primarily because their behavior is determined by layers of complex, proprietary, and undocumented software and hardware. In this demonstration, we share the lessons we learnt developing the uFlip benchmark and conducting experiments with a wide range of flash devices. We illustrate the problems that are actual obstacles to sound performance and energy measurements, and we show how to mitigate the effects of these problems. We also present the uFlip web site and its on-line visualization tool that should help the research community investigate flash device behavior.

The proliferation of the World Wide Web has brought information retrieval (IR) techniques to the forefront of search technology. To the average computer user, “searching” now means using IR-based systems for finding information on the WWW or in other document collections. IR query evaluation methods and workloads differ significantly from those found in database systems. In this paper, we focus on three such differences. First, due to the inherent fuzziness of the natural language used in IR queries and documents, an additional degree of flexibility is permitted in evaluating queries. Second, IR query evaluation algorithms tend to have access patterns that cause problems for traditional buffer replacement policies. Third, IR search is often an iterative process, in which a query is repeatedly refined and resubmitted by the user. Based on these differences, we develop two complementary techniques to improve the efficiency of IR queries: 1) Buffer-aware query evaluation, which alters the query evaluation process based on the current contents of buffers; and 2) Ranking-aware buffer replacement, which incorporates knowledge of the query processing strategy into replacement decisions. In a detailed performance study we show that using either of these techniques yields significant performance benefits and that in many cases, combining them produces even further improvements.

The construction of high-performance database systems that combine the best aspects of the relational and object-oriented approaches requires the design of client-server architectures that can fully exploit client and server resources in a flexible manner. The two predominant paradigms for client-server query execution are data-shipping and query-shipping We first define these policies in terms of the restrictions they place on operator site selection during query optimization. We then investigate the performance tradeoffs between them for bulk query processing. While each strategy has advantages, neither one on its own is efficient across a wide range of circumstances. We describe and evaluate a more flexible policy called hybrid-shipping, which can execute queries at clients, servers, or any combination of the two. Hybrid-shipping is shown to at least match the best of the two "pure" policies, and in some situations, to perform better than both. The implementation of hybrid-shipping raises a number of difficult problems for query optimization. We describe an initial investigation into the use of a 2-step query optimization strategy as a way of addressing these issues.