arXiv subjects

Santiago Andrés Azcoitia

Publications and source records attributed to Santiago Andrés Azcoitia.

5 recordsLinked to original sources

arXiv2025

Direct-to-Cell: A First Look into Starlink's Direct Satellite-to-Device Radio Access Network through Crowdsourced Measurements

Low Earth Orbit (LEO) satellite mega-constellations have emerged as a viable access solution for broadband connectivity in underserved areas. In 2024, Starlink, in partnership with T-Mobile, began beta testing an SMS-only Supplemental Coverage from Space (SCS) service. This marks the first large-scale deployment of Direct Satellite-to-Device (DS2D) communications, allowing unmodified smartphones to connect directly to spaceborne base stations. This paper presents the first measurement study of deployed DS2D technologies. Using crowdsourced mobile network data from the U.S. between October 2024 and July 2025, we provide evidence-based insights into the capabilities, limitations, and future evolution of DS2D technologies for extending mobile connectivity. We find a strong correlation between the number of satellites deployed, the number of unique cell identifiers measured, and the volume of measurements, concentrated in accessible areas with poor terrestrial network coverage, such as national parks and sparsely populated counties. Stable physical-layer measurements were observed throughout the period, with a 24-dB lower median RSRP and a 3-dB higher RSRQ compared to terrestrial networks, reflecting the SMS-only usage of the DS2D network during this period. Based on the SINR measurements collected, we estimate the expected performance of the announced DS2D mobile data service to be around 3 Mbps per beam in outdoor conditions. We also discuss strategies to expand this capacity up to 18 Mbps in the future, depending on key regulatory and business decisions, including allowable out-of-band emissions, permitted number of satellites, and availability of spectrum and orbital resources.

cs.NI↗

arXiv2022

A Survey of Data Marketplaces and Their Business Models

"Data" is becoming an indispensable production factor, just like land, infrastructure, labor or capital. As part of this, a myriad of applications in different sectors require huge amounts of information to feed models and algorithms responsible for critical roles in production chains and business processes. Tasks ranging from automating certain functions to facilitating decision-making in data-driven organizations increasingly benefit from acquiring data inputs from third parties. Responding to this demand, new entities and novel business models have appeared with the aim of matching such data requirements with the right providers and facilitating the exchange of information. In this paper, we present the results and conclusions of a comprehensive survey on the state of the art of entities trading data on the internet, as well as novel data marketplace designs from the research community.

cs.DB↗

arXiv2021

What Is the Price of Data? A Measurement Study of Commercial Data Marketplaces

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers fuel their marketing process, train their ML models, and perform other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem and shed light on several totally unknown facts about it. For example, we show that the median price of live data products sold under a subscription model is around US\$1,400 per month. For one-off purchases of static data, the median price is around US\$2,200. We analyse the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing prices across different DMs as well as a regression analysis for revealing features that correlate with data product prices.

cs.CY↗

arXiv2020

Try Before You Buy: A practical data purchasing algorithm for real-world data marketplaces

Data trading is becoming increasingly popular, as evident by the appearance of scores of Data Marketplaces (DMs) in the last few years. Pricing digital assets is particularly complex since, unlike physical assets, digital ones can be replicated at zero cost, stored, and transmitted almost for free, etc. In most DMs, data sellers are invited to indicate a price, together with a description of their datasets. For data buyers, however, deciding whether paying the requested price makes sense, can only be done after having used the data with their AI/ML algorithms. Theoretical works have analysed the problem of which datasets to buy, and at what price, in the context of full information models, in which the performance of algorithms over any of the O(2^N) possible subsets of N datasets is known a priori, together with the value functions of buyers. Such information is, however, difficult to compute, let alone be made public in the context of real-world DMs. In this paper, we show that if a DM provides to potential buyers a measure of the performance of their AI/ML algorithm on individual datasets, then they can select which datasets to buy with an efficacy that approximates that of a complete information model. We call the resulting algorithm Try Before You Buy (TBYB) and demonstrate over synthetic and real-world datasets how TBYB can lead to near optimal buying performance with only O(N) instead of O(2^N) information released by a marketplace.

cs.DS↗

arXiv2020

Computing the Relative Value of Spatio-Temporal Data in Wholesale and Retail Data Marketplaces

Spatio-temporal information is used for driving a plethora of intelligent transportation, smart-city, and crowd-sensing applications. Since data is now considered a valuable production factor, data marketplaces have appeared to help individuals and enterprises bring it to market to satisfy the ever-growing demand. In such marketplaces, several sources may need to combine their data in order to meet the requirements of different applications. In this paper we study the problem of estimating the relative value of different spatio-temporal datasets combined in wholesale and retail marketplaces for the purpose of predicting demand in metropolitan areas. Using as case studies large datasets of taxi rides from Chicago and New York, we ask questions such as "When does it make sense for different taxi companies to combine their data?", and "How should different companies be compensated for the data that they share?". We then turn our attention to the even harder problem of establishing the relative value of the data brought to retail marketplaces by individual drivers. Overall, we show that simplistic but popular approaches for estimating the relative value of data, such as using volume, or the ``leave-one-out'' heuristic, are inaccurate. Instead, more complex notions of value from economics and game-theory, such as the Shapley value need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. Applying the Shapley value to large datasets from many sources is, of course, computationally challenging. We resort to structured sampling and manage to compute accurately the importance of thousands of data sources. We show that the relative value of the data held by different taxi companies and drivers may differ substantially, and that its relative ranking may change from district to district within a metropolitan area.

cs.SI↗