the termconceptto denote the underlying notion.

It is often assumed that probabilistic facts do not unify with other probabilisticfacts or heads of rules

Legal groundings of such facts can also be restricted by providing a domain, asin the following variant of our alarm example where all persons have the sameprobability of independently hearing the alarm

All ground instances o

b^e^hm^:hj

we obtain the success probabilityofcalls(mary),P(calls(mary)) = 0:196

to provecall

backward reasoning

orward reasoning

queryq

unique least Herbrand model (i.e., a unique least model using onlysymbols from the program

0:1 ::burglary:0:7 ::hearsalarm(mary):0:2 ::earthquake:0:4 ::hearsalarm(john):

The corresponding logicprogram obtained by adding the set of rulesRto the set of facts, also called apossible world

alarm:earthquake:(1)alarm:burglary:calls(X):alarm;hearsalarm(X):call:calls(X)

To summarize, the key contributions of this paper ar

Inference, that is, evaluating the probability distribution dened by a programor model

Typical probabilistic programming languages, on the other hand, employa variant of Sato's distribution semantics (Sato, 1995), in which random variablesdirectly correspond to ground facts and a traditional program species how to de-duce further knowledge from these facts

probabilistic extensions of logic programming languages

statistical relational learningmodels

alternative probabilistic programming language

By now, it is commonly accepted that the more interestingquestion is concerned with the underlying concepts that these languages employand their eect on the inference mechanisms, as their expressive power is oftenvery similar.

To obtain a better understanding of probabilistic programming, we identify anumber of core programming concepts underlying the primitives used by variousprobabilistic languages, discuss the execution mechanisms that they require anduse these to position and survey state-of-the-art probabilistic languages and theirimplementation.While doing so, we focus on probabilistic extensions oflogicprogramminglanguages such as Prolog, which have been considered for over 20 years.

We performed experiments on two real data sets: the first is a pro-prietary address data set and the second is the publicly availableDBLP data set. The address data set contains 1 million strings,each a concatenation of an organization name and address (street,city, zip, state). The DBLP data set contains around 0.5 millionstrings, each a concatenation of authors and title of a publication.

n summary, ER is an inherently expensive process, sothat only relatively small sets of records can be handled.Thus, large data sets need to be partitioned into smaller setsthat can be resolved in detail. How large a data set can beexhaustively resolved depends on the application. It is alsopossible to distribute the ER computations across multipleprocessors, in order to handle larger data sets. In [7] westudy various strategies for distributing the work done byR-Swoosh.

F-Swoosh

R-Swoosh:

G-Swoosh

We identify the ICAR properties

The particular variant of the ER problem that we studyin this paper may not be the most sophisticated

Un-like other works that focus only on identifying matchingrecords

Because record matching is inherently expensive, largesets of input records are often divided into “buckets” usingapplication knowledge, and then ER is run on each bucket.

One key challenge for the classication-based approachinvolves the selection of training examples from which tolearn the similarity classiers. Ideally, we want our modelto correctly predict the similarity of every document to everyother document (or every centroid, based on the modelingchoice described above) in the dataset. However, creating atraining example for each document (or document-centroid)pair results in a skewed label distribution, since a large ma-jority of pairs in the training dataset do not belong to thesame event

Radiohead

[6] M. Hernandez and S. Stolfo. Real-world data is dirty:data cleansing and the merge/purge problem.Journalof Data Mining and Knowledge Discovery, 1(2), 1998.

The experimental comparisons should be extended to non-DBGendata sets to investigate dependencies on data sourcesand their error characteristics.

Weobservedsimilarresultsonthepublicationinformationofabibliographydatabase.Weomitresultsduetospaceconstraints

Table 2. Target databases.

Had this model failed to identify a bug, we could subsequently haveenriched the sketched specifications.

In this survey I explain h

Let C be a bivalent configuration of P, and let e = (p, m) be an event that is applicable to C. Let %? be the set of configurations reachable from C without applying e, and let 9 = e(g) = (e(E) I E E %? and e is applicable to E). Then, 9 contains a bivalent configuration.

assumed partial correctness.

The basic idea is to show circumstances under which the protocol remains forever indecisive. This involves two steps. First, we argue that there is some initial configuration in which the decision is not already predetermined. Second, we construct an admissible run that avoids ever taking a step that would commit the system to a particular decision.

The associated sequence of steps is called a run.

Thus, the message system acts nondeterministically, subject only to the condition that if receive( p) is performed infinitely many times, then every message (p, m) in the message buffer is eventually delivered. In particular, the message system is allowed to return 0 a finite number of times in response to receive(p), even though a message (p, m) is present in the buffer

Our system model is rather strong

. In one atomic step, a process can attempt to receive a message, perform local computation on the basis of

adaptsefficientlyasnodesjoinandleavethesystem,andcananswerqueriesevenifthesystemiscontinuouslychanging

some application developers may not want to write their own conflict resolution mechanisms and choose to push it down to the data store

give services control over their system properties, such as durability and consistency, and to let services make their own tradeoffs between functionality, performance and cost-effectiveness

State management then becomes the main component of a service’s SLA.

which are in general measured at the 99.9th percentile of the distribution.

object versioning

An SLA stated in terms of mean or median response times will not address the performance of this important customer segment.

non-hostile and there are no security related requirements such as authentication and authorizatio

eventually-consistent storage system can be used in production with demanding application

In the past year, Dynamo has been the underlying storage technology for a number of the core services in Amazon’s e-commerce platform. It was able to scale to extreme peak loads efficiently without any downtime during the busy holiday shopping season. For example, the service that maintains shopping cart (Shopping Cart Service) served tens of millions requests that resulted in well over 3 million checkouts in a single day and the service that manages session state handled hundreds of thousands of concurrently active sessions.

scalability and availabilit

To achieve this level of availability, Dynamo sacrifices consistency

destroyed by tornados

Exception Handling

Violation of this principle seemed likely to lead us into the complexities that have made previous communication packages and protocols difficult to use

imbedded in our major language, Mesa

use procedure calls as the paradigm for expressing control and data transfers. For example, message passing might be a plausible alternative.

he primary purpose

these should make it easier to build distributed computations, and to get them right. Another is efficiency: procedure calls seem simple enough for the communication to be quite rapid. A third is generality: in singie-machine com- putations, procedures are often the most important mechanism for communica- tion between parts of the algorithm.

these should make it easier to build distributed computations, and to get them right.

that all current invocations or callsfor system services will be eventually converted into callsthat might be to an object residing on some other machine.

for easily under-standable reasons

Unless the naming interfaceincludes an operation to lock a naming context, there is noway that I can make this operation completely robust.

This is not to argue against pleasant programming inter-faces.

The hard problems have to do with differ-ences in memory access paradigms between local and dis-tributed entities.

Thehard problems in distributed computing concern dealingwith partial failure and the lack of a central resource man-ager.

A less optimistic explanation of the failure of each attemptat unification holds that any such attempt will fail for thesimple reason that programming distributed applications isnot the same as programming non-distributed applications

what-ever programming model is currently in vogue