text:
@inproceedings{10.1145/1592748.1592749,
author = {Provost, Foster},
title = {Brand Advertising, on-Line Audiences, and Social Media: Invited Talk},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592749},
doi = {10.1145/1592748.1592749},
abstract = {This talk will discuss the issues and techniques at the intersection of data science, advertising technology, and the business of brand advertising on-line. For better or worse, on-line advertising has been viewed by many advertisers as a direct marketing channel, with sponsored search advertising being the shining star. However, a very large proportion of advertising budgets is dedicated to brand advertising, for which clicks and short-horizon on-line purchases may not be the primary goal. Instead or in addition, brand advertisers want to get an advertising message to a chosen audience, for example to build or to reinforce brand affinity. This talk will argue that social media may be particularly valuable for on-line brand advertising, and discuss technology for selecting, evaluating, and targeting audiences, plus important issues such as privacy and maintaining brand image.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {vi},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592750,
author = {Li, Ting and Liu, Ning and Yan, Jun and Wang, Gang and Bai, Fengshan and Chen, Zheng},
title = {A Markov Chain Model for Integrating Behavioral Targeting into Contextual Advertising},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592750},
doi = {10.1145/1592748.1592750},
abstract = {Both Contextual Advertising (CA) and Behavioral Targeting (BT) are playing important roles in online advertising market. Recently, the problem of how to integrate BT strategies into CA has attracted much attention from both industry and academia. However, to our best knowledge, few research works have been published to provide BT solutions in CA. In this paper, we propose a new notion of relevance between webpages and ads based on users' online click-through behaviors from BT's perspective. Compared with the classical behavior targeting method where only users' history interests are considered, we pay more attention to the click probability of ads from a webpage where the relevance between them is evaluated. Moreover, a combination model integrating behavioral relevance and contextual relevance for matching ads and webpags is presented. The model parameters are learnt from a dataset consisting of 200 webpages and 35,880 ads. Experimental results show that our integrated strategy indeed outperforms the strategies that only consider either behavioral relevance or contextual relevance. The best model achieves a 18.1% improvement in precision over single strategies.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {1–9},
numpages = {9},
keywords = {Markov chain model, behavioral targeting, click-through data, contextual advertising},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592751,
author = {Wu, Xiaohui and Yan, Jun and Liu, Ning and Yan, Shuicheng and Chen, Ying and Chen, Zheng},
title = {Probabilistic Latent Semantic User Segmentation for Behavioral Targeted Advertising},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592751},
doi = {10.1145/1592748.1592751},
abstract = {Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment users for ads delivery, and good user segmentation may significantly improve the ad click-through rate (CTR). Different from classical user segmentation strategies, which rarely take the semantics of user behaviors into consideration, we propose in this paper a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic user segmentation study for BT in academia.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {10–17},
numpages = {8},
keywords = {behavioral targeting (BT), probabilistic latent semantic analysis, user segmentation},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592752,
author = {Wang, Xin-Jing and Yu, Mo and Zhang, Lei and Cai, Rui and Ma, Wei-Ying},
title = {Argo: Intelligent Advertising by Mining a User's Interest from His Photo Collections},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592752},
doi = {10.1145/1592748.1592752},
abstract = {In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users' photo collections. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, the Argo system attempts to learn a user's profile from his shared photos and suggests relevant ads accordingly. To learn a user interest, in an offline step, a hierarchical and efficient topic space is constructed based on the ODP ontology, which is used later on for bridging the vocabulary gap between ads and photos as well as reducing the effect of noisy photo tags. In the online stage, the process of Argo contains three steps: 1) understanding the content and semantics of a user's photos and auto-tagging each photo to supplement user-submitted tags (such tags may not be available); 2) learning the user interest given a set of photos based on the learnt hierarchical topic space; and 3) representing ads in the topic space and matching their topic distributions with the target user interest; the top ranked ads are output as the suggested ads. Two key challenges are tackled during the process: 1) the semantic gap between the low-level image visual features and the high-level user semantics; and 2) the vocabulary impedance between photos and ads. We conducted a series of experiments based on real Flickr users and Amazon.com products (as candidate ads), which show the effectiveness of the proposed approach.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {18–26},
numpages = {9},
keywords = {user interest modeling, photo monetization, image understanding},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592753,
author = {Schwaighofer, Anton and Candela, Joaquin Qui\~{n}onero and Borchert, Thomas and Graepel, Thore and Herbrich, Ralf},
title = {Scalable Clustering and Keyword Suggestion for Online Advertisements},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592753},
doi = {10.1145/1592748.1592753},
abstract = {We present an efficient Bayesian online learning algorithm for clustering vectors of binary values based on a well known model, the mixture of Bernoulli profiles. The model includes conjugate Beta priors over the success probabilities and maintains discrete probability distributions for cluster assignments. Clustering is then formulated as inference in a factor graph which is solved efficiently using online approximate message passing. The resulting algorithm has three key features: a) it requires only a single pass across the data and can hence be used on data streams, b) it maintains the uncertainty of parameters and cluster assignments, and c) it implements an automatic step size adaptation based on the current model uncertainty. The model is tested on an artificially generated toy dataset and applied to a large scale real-world data set from online advertising, the data being online ads characterized by the set of keywords to which they have been subscribed. The proposed approach scales well for large datasets, and compares favorably to other clustering algorithms on the ads dataset. As a concrete application to online advertising we show how the learnt model can be used to recommend new keywords for given ads.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {27–36},
numpages = {10},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592754,
author = {Sarmento, Lu\'{\i}s and Trezentos, Paulo and Gon\c{c}alves, Jo\~{a}o Pedro and Oliveira, Eug\'{e}nio},
title = {Inferring Local Synonyms for Improving Keyword Suggestion in an On-Line Advertisement System},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592754},
doi = {10.1145/1592748.1592754},
abstract = {In this paper we present a keyword suggestion mechanism for supporting advertisers wishing to publish ads in content-targeted advertisement systems. The method infers "synonymy" between keywords by mining a database of previously submitted ads, and uses such information for suggesting relevant and non-obvious keywords to advertisers. Automatic word-sense disambiguation is provided implicitly by our keyword ranking procedure. We perform on-line comparison of our method with another keyword suggestion system being currently used in the largest Portuguese web advertisement broker with presence in four different countries, by redirecting 50% of keyword suggestion requests to each of the two systems. We propose a novel set of evaluation measures to compare the performance of keyword suggestion systems in such experimental setting. Results show that ads containing keywords suggested by the method we propose are selected for being printed more frequently at similar, or often superior, click-through rates. This results in a potential global revenue increase for the ad broker.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {37–45},
numpages = {9},
keywords = {automatic keyword suggestion, web advertisement},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592755,
author = {Shaparenko, Benyah and \c{C}etin, \"{O}zg\"{u}r and Iyer, Rukmini},
title = {Data-Driven Text Features for Sponsored Search Click Prediction},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592755},
doi = {10.1145/1592748.1592755},
abstract = {Much search engine revenue comes from sponsored search ads displayed with algorithmic search results. To maximize revenue, it is essential to choose a good slate of ads for each query, requiring accurate prediction of whether or not users will click on an ad. Click prediction is relatively easy for the ads that have been displayed many times, and have significant click history, but in the long tail with minimal or no click history, other features are needed to predict user response. In this work, we investigate the use of novel text features for this problem, within the context of a state-of-the-art sponsored search system. In particular, we propose the use of detailed word-pair indicator features between the query and ad. We compare the new features to the traditional vector-space and language modeling features extracted in a typical information-retrieval style. We evaluate these approaches in a maximum-entropy ranking model using the click-view data from a commercial search-engine traffic. We show that the word-pair features are highly helpful for sponsored search click prediction, not only improving over the sophisticated click-history feedback based systems, but also compensating for the lack of click history to some extent. In contrast, we find that the language and vector-space modeling approaches are significantly less effective.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {46–54},
numpages = {9},
keywords = {text features, sponsored search, click prediction, vector-space model, language model, maximum entropy model},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592756,
author = {Zhu, Yunzhang and Wang, Gang and Yang, Junli and Wang, Dakan and Yan, Jun and Chen, Zheng},
title = {Revenue Optimization with Relevance Constraint in Sponsored Search},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592756},
doi = {10.1145/1592748.1592756},
abstract = {Displaying sponsored ads alongside the search results is a key monetization strategy for search engine companies. Since users are more likely to click ads that are relevant to their query, it is crucial for search engine to deliver the right ads for the query and the order in which they are displayed. There are several works investigating on how to learn a ranking function to maximize the number of ad clicks. However, this ranking optimization problem is different from algorithmic search results ranking in that the ranking scheme must take received revenue into account in order to make more profit for the search engines. In this paper, we address a new optimization problem and aim to answer the question: how to construct a ranking model that can deliver high quality ads to the user as well as maximize search engine revenue? We introduce a novel tradeoff method from machine learning perspective, and through this method we have the privilege of choosing a tradeoff parameter to achieve highest relevance ranking or highest revenue ranking or the tradeoff between them. The algorithms are built upon the click-through log data with real ad clicks and impressions. The extensively experimental results verify that the proposed algorithm has the property that the search engine could choose a proper parameter to achieve high revenue(income) without losing to much relevance.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {55–60},
numpages = {6},
keywords = {sponsored search, revenue optimization, machine learning, ranking},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592757,
author = {Juda, Adam Isaac and Muthukrishnan, S. and Rastogi, Ashish},
title = {Pricing Guidance in Ad Sale Negotiations: The PrintAds Example},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592757},
doi = {10.1145/1592748.1592757},
abstract = {We consider negotiations between publishers and advertisers in a marketplace for ads. Motivated by Google's online PrintAds system which is such a marketplace, we focus on the role of the market runner in improving market efficiency. We abstract the problem of pricing guidance where the market runner provides an initial price-point for negotiations based on data analysis. The problem is nuanced because the market runner can not fully reveal the price data for any of the publishers. We introduce two solutions for pricing guidance, the first using clustering and the second using support vector machines, and present experimental evaluation of our methods. Pricing guidance by the market runner is a novel direction, and we expect more research in the future.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {61–68},
numpages = {8},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592758,
author = {Alaei, Saeed and Arcaute, Esteban and Khuller, Samir and Ma, Wenjing and Malekian, Azarakhsh and Tomlin, John},
title = {Online Allocation of Display Advertisements Subject to Advanced Sales Contracts},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592758},
doi = {10.1145/1592748.1592758},
abstract = {In this paper we propose a utility model that accounts for both sales and branding advertisers. We first study the computational complexity of optimization problems related to both online and offline allocation of display advertisements. Next, we focus on a particular instance of the online allocation problem, and design a simple online algorithm with provable approximation guarantees. Our algorithm is near optimal as is shown by a matching lower bound. Finally, we report on experiments to establish actual case behavior on some real datasets, with encouraging results.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {69–77},
numpages = {9},
keywords = {display advertising, optimization, modeling, online algorithms, simulation},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592759,
author = {May, Michael and K\"{o}rner, Christine and Hecker, Dirk and Pasquier, Martial and Hofmann, Urs and Mende, Felix},
title = {Handling Missing Values in GPS Surveys Using Survival Analysis: A GPS Case Study of Outdoor Advertising},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592759},
doi = {10.1145/1592748.1592759},
abstract = {GPS technology has made it possible to evaluate the performance of outdoor advertising campaigns in an objective manner. Given the GPS trajectories of a sample of test persons over several days, their passages with arbitrary poster campaigns can be calculated. However, inference is complicated by the early dropout of persons. Other than in most demonstrations of spatial data mining algorithms where the structure of the data sample is usually disregarded, poster performance measures such as reach and gross impressions evolve continuously over time and require non-intermittent observations. In this paper, we investigate the applicability of survival analysis to compensate for missing measurement days. We formalize the task of modeling the visit potential of geographic locations based on trajectory data as our variable of interest results from dispersed events in space-time. We perform experiments on the cities of Zurich and Bern simulating different dropout mechanisms and dropout rates and show the adequacy of the applied method. Our modeling technique is at present part of a business solution for the Swiss outdoor advertising branch and serves as pricing basis for the majority of Swiss poster locations.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {78–84},
numpages = {7},
keywords = {missing values, outdoor advertising, mobility mining, GPS, survival analysis},
location = {Paris, France},
series = {ADKDD '09}
}

@inproceedings{10.1145/1592748.1592760,
author = {Interian, Yannet and Dorai-Raj, Sundar and Naverniouk, Igor and Opalinski, P. J. and Kaustuv and Zigmond, Dan},
title = {Ad Quality on TV: Predicting Television Audience Retention},
year = {2009},
isbn = {9781605586717},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1592748.1592760},
doi = {10.1145/1592748.1592760},
abstract = {This paper explores the impact of television advertisements on audience retention using data collected from television set-top boxes (STBs). In particular, we discuss how the accuracy of the retention score, a measure of ad quality, is improved by using the recent "click history" of the STBs tuned to the ad. These retention scores are related to -- and are a natural extension of -- other measures of ad quality that have been used in online advertising since at least 2005 [2]. Like their online counterparts, TV retention scores could be used to determine if an ad should be eligible to enter the inventory auction and, if it is, how highly the ad should be ranked [1]. A retention score (RS) could also be used by the auction system for pricing, or by the advertiser to compare different creatives for the same product.},
booktitle = {Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising},
pages = {85–91},
numpages = {7},
location = {Paris, France},
series = {ADKDD '09}
}

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr


text:
package mail

import (
	"context"
	"fmt"
	ht "html/template"
	"io/ioutil"
	"net/smtp"
	"strings"
	tt "text/template"

	"github.com/jordan-wright/email"
	"github.com/rotisserie/eris"

	"github.com/ngld/knossos/packages/server/pkg/config"
	"github.com/ngld/knossos/packages/server/pkg/nblog"
)

// ResetMailParams contains the values that will be passed to the mail template
type ResetMailParams struct {
	To       string
	Found    bool
	Username string
	Token    string
	// This field will be set automatically
	BaseURL string
}

var (
	resetText *tt.Template
	resetHTML *ht.Template
)

func initReset(cfg *config.Config) error {
	data, err := ioutil.ReadFile(cfg.Mail.Reset.Text)
	if err != nil {
		return eris.Wrapf(err, "failed to read template file %s", cfg.Mail.Reset.Text)
	}

	resetText, err = tt.New("reset mail template").Parse(string(data))
	if err != nil {
		return eris.Wrapf(err, "failed to parse template %s", cfg.Mail.Reset.Text)
	}

	data, err = ioutil.ReadFile(cfg.Mail.Reset.HTML)
	if err != nil {
		return eris.Wrapf(err, "failed to read template file %s", cfg.Mail.Reset.HTML)
	}

	resetHTML, err = ht.New("reset mail html").Parse(string(data))
	if err != nil {
		return eris.Wrapf(err, "failed to parse template %s", cfg.Mail.Reset.HTML)
	}

	return nil
}

// SendResetMail sends a password reset mail
func SendResetMail(ctx context.Context, cfg *config.Config, params ResetMailParams) error {
	nblog.Log(ctx).Debug().Msgf("Sending reset mail to %s", params.To)

	params.BaseURL = cfg.HTTP.BaseURL

	mail := email.NewEmail()
	mail.From = cfg.Mail.From
	mail.Subject = cfg.Mail.Reset.Subject
	mail.To = []string{params.To}

	text := strings.Builder{}
	err := resetText.Execute(&text, params)
	if err != nil {
		return eris.Wrap(err, "failed to execute password reset text template")
	}

	mail.Text = []byte(text.String())

	text.Reset()
	err = resetHTML.Execute(&text, params)
	if err != nil {
		return eris.Wrap(err, "failed to execute password reset HTML template")
	}

	mail.HTML = []byte(text.String())

	auth := smtp.PlainAuth("", cfg.Mail.Username, cfg.Mail.Password, cfg.Mail.Server)
	addr := fmt.Sprintf("%s:%d", cfg.Mail.Server, cfg.Mail.Port)

	switch cfg.Mail.Encryption {
	case "STARTTLS":
		err = mail.SendWithStartTLS(addr, auth, nil)
	case "SSL":
		err = mail.SendWithTLS(addr, auth, nil)
	default:
		err = mail.Send(addr, auth)
	}

	if err != nil {
		return eris.Wrap(err, "failed to send mail")
	}

	nblog.Log(ctx).Debug().Msg("Mail successfully sent")
	return nil
}

repository: ngld/knossos

license_type: permissive

repo_name: ngld/knossos

branch_name: refs/heads/nu

gha_language: Go

extension: go


text:
%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Christoph Durr at 2015-12-11 17:58:16 +0100

%% Saved with string encoding Occidental (Windows Latin 1)

@article{BadicsBoros:98:HP-minimization,
	Author = {Badics, T. and Boros, E.},
	Journal = {Mathematics of Operations Research},
	Number = {3},
	Pages = {649-600},
	Title = {Minimization of half-products},
	Volume = {23},
	Year = {1998}}

@article{ErelGhosh:08:FPTAS-HP-minimization,
	Author = {Erdal Erel and Jay B. Ghosh},
	Journal = {Discrete Applied Mathematics},
	Number = {15},
	Pages = {3046-3056},
	Title = {{FPTAS} for half-products minimization with scheduling applications},
	Volume = {156},
	Year = {2008}}

@article{IbarraKim:75:Subsetsum-Algorithm,
	Address = {New York, NY, USA},
	Author = {Ibarra, Oscar H. and Kim, Chul E.},
	Journal = {Journal of the ACM (JACM)},
	Number = {4},
	Pages = {463--468},
	Publisher = {ACM},
	Title = {Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems},
	Volume = {22},
	Year = {1975}}

@article{JurischKubiakJozefowska:97:Minclique-algorithm,
	Author = {Bernd Jurisch and Wieslaw Kubiak and Joanna Jozefowska},
	Journal = {Discrete Applied Mathematics},
	Number = 1,
	Pages = {115--139},
	Title = {Algorithms for minclique scheduling problems},
	Volume = 72,
	Year = 1997}

@article{GrahamLawlerLenstraKan:79:Problem-Notation,
	Author = {Graham, R.L. and Lawler, E.L. and Lenstra, J.K. and Kan, A.H.G.R.},
	Journal = {Annals of Discrete Mathematics},
	Number = {2},
	Pages = {287--326},
	Title = {Optimization and approximation in deterministic sequencing and scheduling: A survey},
	Volume = {5},
	Year = {1979}}

@book{GamowStern:58:Puzzle-Math,
	Author = {Gamow, George and Stern, Marvin},
	Publisher = {Macmillan},
	Title = {Puzzle-math},
	Year = {1958}}

@book{GareyJohnson:79:Complexity,
	Author = {M. R. Garey and David S. Johnson},
	Publisher = {W. H. Freeman},
	Title = {Computers and Intractability: A Guide to the Theory of NP-Completeness},
	Year = {1979}}

@book{Nisan:07:Algorithmic-game-theory,
	Author = {Nisan, Noam},
	Publisher = {Cambridge University Press},
	Title = {Algorithmic game theory},
	Year = {2007}}

@book{EasleyKleinberg:10:Networks,
	Author = {Easley, David and Kleinberg, Jon},
	Publisher = {Cambridge University Press},
	Title = {Networks, crowds, and markets},
	Year = {2010}}

@article{Woeginger:10:OpenProblems,
	Address = {Dagstuhl, Germany},
	Author = {Gerhard J. Woeginger},
	Editor = {Susanne Albers and Sanjoy K. Baruah and Rolf H. M{\"o}hring and Kirk Pruhs},
	Issn = {1862-4405},
	Journal = {Dagstuhl Research Online Publication Server},
	Pages = {24},
	Publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
	Title = {{Scheduling (Dagstuhl Seminar 10071)}},
	Url = {http://drops.dagstuhl.de/opus/volltexte/2010/2536},
	Year = {2010},
	Bdsk-Url-1 = {http://drops.dagstuhl.de/opus/volltexte/2010/2536}}

@article{Hohn:13:OpenProblems,
	Address = {Dagstuhl, Germany},
	Author = {W. H{\"o}hn},
	Editor = {Susanne Albers and Onno J. Boxma and Kirk Pruhs},
	Issn = {2192-5283},
	Journal = {Dagstuhl Research Online Publication Server},
	Pages = {32--33},
	Publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
	Title = {{Scheduling (Dagstuhl Seminar 13111)}},
	Url = {http://drops.dagstuhl.de/opus/volltexte/2013/4024},
	Year = {2013},
	Bdsk-Url-1 = {http://drops.dagstuhl.de/opus/volltexte/2013/4024}}

@article{HartNilssonBertram:72:Proposition-A-star-algorithm,
	Author = {Hart, Peter E and Nilsson, Nils J and Raphael, Bertram},
	Journal = {ACM SIGART Bulletin},
	Pages = {28--29},
	Publisher = {ACM},
	Title = {Correction to a formal basis for the heuristic determination of minimum cost paths},
	Volume = {37},
	Year = {1972}}

@article{SenBagchiRamaswamy:96:A-star-algorithm-for-scheduling-problems,
	Author = {Sen, Anup K and Bagchi, Amitava and Ramaswamy, Ramkumar},
	Journal = {IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans},
	Number = {1},
	Pages = {168--173},
	Publisher = {IEEE},
	Title = {Searching graphs with {A*}: applications to job sequencing},
	Volume = {26},
	Year = {1996}}

@article{KaindlKainzRadda:01:Asymmetry-in-search-for-A-star-algorithm,
	Author = {Kaindl, H. and Kainz, G. and Radda, K.},
	Journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics},
	Number = {5},
	Pages = {791--796},
	Publisher = {IEEE},
	Title = {Asymmetry in search},
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@unpublished{Verschae:13:FPTAS-weighted-tardiness-common-due-date-completion-time-penalty-function,
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@article{SchildFredman:62:Order-constraint-quadratic-penalty-function-with-t*,
	Author = {Schild, A. and Fredman, I.J.},
	Journal = {Management Science},
	Number = {1},
	Pages = {73--81},
	Publisher = {INFORMS},
	Title = {Scheduling tasks with deadlines and non-linear loss functions},
	Volume = {9},
	Year = {1962}}

license: MIT

repository: xtof-durr/schedulingzoo

repo_name: xtof-durr/schedulingzoo

branch_name: refs/heads/main

extension: bib


text:
# MIT License
#
# # Copyright (c) 2022 Ignacio Vizzo, Cyrill Stachniss, University of Bonn
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

include(ExternalProject)
ExternalProject_Add(
  external_blosc
  PREFIX blosc
  URL https://github.com/Blosc/c-blosc/archive/refs/tags/v1.5.0.tar.gz
  URL_HASH SHA256=208ba4db0e5116421ed2fbbdf2adfa3e1d133d29a6324a0f47cf2d71f3810c92
  UPDATE_COMMAND ""
  CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=<INSTALL_DIR>
             ${ExternalProject_CMAKE_ARGS}
             ${ExternalProject_CMAKE_CXX_FLAGS}
             # Custom OpenVDB build settings
             -DBUILD_STATIC=ON
             -DBUILD_TESTS=OFF
             -DBUILD_BENCHMARKS=OFF
             -DPREFER_EXTERNAL_COMPLIBS=OFF)

# Simulate importing Blosc::blosc for OpenVDBHelper target
ExternalProject_Get_Property(external_blosc INSTALL_DIR)
set(BLOSC_ROOT ${INSTALL_DIR} CACHE INTERNAL "BLOSC_ROOT Install directory")
add_library(BloscHelper INTERFACE)
add_dependencies(BloscHelper external_blosc)
target_include_directories(BloscHelper INTERFACE ${INSTALL_DIR}/include)
target_link_directories(BloscHelper INTERFACE ${INSTALL_DIR}/lib)
target_link_libraries(BloscHelper INTERFACE blosc.a)
add_library(Blosc::blosc ALIAS BloscHelper)

repository: PRBonn/vdbfusion

repo_name: PRBonn/vdbfusion

gha_license_id: MIT

extension: cmake